Breast Cancer

Key clinical point: Sarcoidosis can develop after breast cancer and requires histologic confirmation.

Major finding: Twenty of 429 (4.9%) women with sarcoidosis had breast cancer, which usually was diagnosed first. Sarcoidosis was diagnosed at a median age of 53.9 years, most often involved the lungs and central lymph nodes, and was asymptomatic in half of cases.

Study details: Single-center retrospective study of 1,000 sarcoidosis cases.

Disclosures: The study was not funded. The investigators reported having no conflicts.

Citation: Papanikolaou IC et al. Resp Med Case Rep. 2020 Aug 13.  doi: 10.1016/j.rmcr.2020.101190

Key clinical point: Sarcoidosis can develop after breast cancer and requires histologic confirmation.

Major finding: Twenty of 429 (4.9%) women with sarcoidosis had breast cancer, which usually was diagnosed first. Sarcoidosis was diagnosed at a median age of 53.9 years, most often involved the lungs and central lymph nodes, and was asymptomatic in half of cases.

Study details: Single-center retrospective study of 1,000 sarcoidosis cases.

Disclosures: The study was not funded. The investigators reported having no conflicts.

Citation: Papanikolaou IC et al. Resp Med Case Rep. 2020 Aug 13.  doi: 10.1016/j.rmcr.2020.101190

Key clinical point: Sarcoidosis can develop after breast cancer and requires histologic confirmation.

Major finding: Twenty of 429 (4.9%) women with sarcoidosis had breast cancer, which usually was diagnosed first. Sarcoidosis was diagnosed at a median age of 53.9 years, most often involved the lungs and central lymph nodes, and was asymptomatic in half of cases.

Study details: Single-center retrospective study of 1,000 sarcoidosis cases.

Disclosures: The study was not funded. The investigators reported having no conflicts.

Citation: Papanikolaou IC et al. Resp Med Case Rep. 2020 Aug 13.  doi: 10.1016/j.rmcr.2020.101190

Key clinical point: The mTOR inhibitor everolimus performed modestly when sequenced after palbociclib in metastatic HR+ HER2- breast cancer.

Major finding: Median PFS on everolimus was 4.2 months. ORR was 17.1% (all partial responses).

Study details: Two-center retrospective chart review of 41 patients who received everolimus combinations after their metastatic HR+ HER2- breast cancer progressed on palbociclib.

Disclosures: The National Cancer Institute provided funding. Four of the investigators disclosed ties to Novartis, Pfizer, and other pharmaceutical companies.

Citation: Dhakal A et al. Breast Cancer (Auckl). 2020 Jul 23. doi: 10.1177/1178223420944864

Key clinical point: The mTOR inhibitor everolimus performed modestly when sequenced after palbociclib in metastatic HR+ HER2- breast cancer.

Major finding: Median PFS on everolimus was 4.2 months. ORR was 17.1% (all partial responses).

Study details: Two-center retrospective chart review of 41 patients who received everolimus combinations after their metastatic HR+ HER2- breast cancer progressed on palbociclib.

Disclosures: The National Cancer Institute provided funding. Four of the investigators disclosed ties to Novartis, Pfizer, and other pharmaceutical companies.

Citation: Dhakal A et al. Breast Cancer (Auckl). 2020 Jul 23. doi: 10.1177/1178223420944864

Key clinical point: The mTOR inhibitor everolimus performed modestly when sequenced after palbociclib in metastatic HR+ HER2- breast cancer.

Major finding: Median PFS on everolimus was 4.2 months. ORR was 17.1% (all partial responses).

Study details: Two-center retrospective chart review of 41 patients who received everolimus combinations after their metastatic HR+ HER2- breast cancer progressed on palbociclib.

Disclosures: The National Cancer Institute provided funding. Four of the investigators disclosed ties to Novartis, Pfizer, and other pharmaceutical companies.

Citation: Dhakal A et al. Breast Cancer (Auckl). 2020 Jul 23. doi: 10.1177/1178223420944864

Key clinical point: In Brazil, the COVID-19 pandemic changed how specialists managed early breast cancer, especially HR-positive tumors.

Major finding: Nearly 70% of breast cancer specialists reported changing their management of early breast cancer. For more proliferative HR-positive tumors (Ki-67 >30%), 34% recommended neoadjuvant endocrine therapy (NET) for postmenopausal patients, while 10.9% recommended NET for premenopausal patients.

Study details: Survey of 503 breast cancer specialists in Brazil.

Disclosures: The study was not funded. The researchers reported having no conflicts.

Citation: Cavalcante FP et al. Breast Cancer Res Treat. 2020 Aug 16. doi: 10.1007/s10549-020-05877-y

Key clinical point: In Brazil, the COVID-19 pandemic changed how specialists managed early breast cancer, especially HR-positive tumors.

Major finding: Nearly 70% of breast cancer specialists reported changing their management of early breast cancer. For more proliferative HR-positive tumors (Ki-67 >30%), 34% recommended neoadjuvant endocrine therapy (NET) for postmenopausal patients, while 10.9% recommended NET for premenopausal patients.

Study details: Survey of 503 breast cancer specialists in Brazil.

Disclosures: The study was not funded. The researchers reported having no conflicts.

Citation: Cavalcante FP et al. Breast Cancer Res Treat. 2020 Aug 16. doi: 10.1007/s10549-020-05877-y

Key clinical point: In Brazil, the COVID-19 pandemic changed how specialists managed early breast cancer, especially HR-positive tumors.

Major finding: Nearly 70% of breast cancer specialists reported changing their management of early breast cancer. For more proliferative HR-positive tumors (Ki-67 >30%), 34% recommended neoadjuvant endocrine therapy (NET) for postmenopausal patients, while 10.9% recommended NET for premenopausal patients.

Study details: Survey of 503 breast cancer specialists in Brazil.

Disclosures: The study was not funded. The researchers reported having no conflicts.

Citation: Cavalcante FP et al. Breast Cancer Res Treat. 2020 Aug 16. doi: 10.1007/s10549-020-05877-y

Key clinical point: For patients with early breast cancer, ≤ 6 months of adjuvant trastuzumab was noninferior to a 1-year course and appeared to be less cardiotoxic.

Major finding: 5-year DFS rates were 85.4% vs. 87.1%, respectively. Rates of congestive heart failure were 3.9% vs. 6.9%, respectively.

Study details: Meta-analysis of 5 randomized trials (11,376 patients).

Disclosures: Funding sources were not reported. Two coinvestigators disclosed ties to Roche, Eisai, Novartis, Sanofi, Kendle India, and several other pharmaceutical companies.

Citation: Gulia S et al. 2020 Aug 24. JAMA Netw Open. doi: 10.1001/jamanetworkopen.2020.11777

Key clinical point: For patients with early breast cancer, ≤ 6 months of adjuvant trastuzumab was noninferior to a 1-year course and appeared to be less cardiotoxic.

Major finding: 5-year DFS rates were 85.4% vs. 87.1%, respectively. Rates of congestive heart failure were 3.9% vs. 6.9%, respectively.

Study details: Meta-analysis of 5 randomized trials (11,376 patients).

Disclosures: Funding sources were not reported. Two coinvestigators disclosed ties to Roche, Eisai, Novartis, Sanofi, Kendle India, and several other pharmaceutical companies.

Citation: Gulia S et al. 2020 Aug 24. JAMA Netw Open. doi: 10.1001/jamanetworkopen.2020.11777

Key clinical point: For patients with early breast cancer, ≤ 6 months of adjuvant trastuzumab was noninferior to a 1-year course and appeared to be less cardiotoxic.

Major finding: 5-year DFS rates were 85.4% vs. 87.1%, respectively. Rates of congestive heart failure were 3.9% vs. 6.9%, respectively.

Study details: Meta-analysis of 5 randomized trials (11,376 patients).

Disclosures: Funding sources were not reported. Two coinvestigators disclosed ties to Roche, Eisai, Novartis, Sanofi, Kendle India, and several other pharmaceutical companies.

Citation: Gulia S et al. 2020 Aug 24. JAMA Netw Open. doi: 10.1001/jamanetworkopen.2020.11777

Key clinical point: Despite showing efficacy in specific subgroups, immune checkpoint inhibitors (ICIs) are unlikely to benefit most women with metastatic breast cancer.

Major finding: Objective response rates were 19% overall, 27% in PD-L1 positive patients, 18% in PD-L1 negative patients, 28% in HER2-positive breast cancer, 23% in triple-negative breast cancer, 35% when used in the first line, 26% when combined with systemic therapy, and 9% when used as monotherapy.

Study details: Meta-analysis of 27 studies of metastatic breast cancer (1,746 patients).

Disclosures: The study was funded by the National Natural Science Foundation of China. The investigators reported having no conflicts.

Citation: Zou Y et al. Ther Adv Med Oncol. 2020 Aug 17. doi: 10.1177/1758835920940928

Key clinical point: Despite showing efficacy in specific subgroups, immune checkpoint inhibitors (ICIs) are unlikely to benefit most women with metastatic breast cancer.

Major finding: Objective response rates were 19% overall, 27% in PD-L1 positive patients, 18% in PD-L1 negative patients, 28% in HER2-positive breast cancer, 23% in triple-negative breast cancer, 35% when used in the first line, 26% when combined with systemic therapy, and 9% when used as monotherapy.

Study details: Meta-analysis of 27 studies of metastatic breast cancer (1,746 patients).

Disclosures: The study was funded by the National Natural Science Foundation of China. The investigators reported having no conflicts.

Citation: Zou Y et al. Ther Adv Med Oncol. 2020 Aug 17. doi: 10.1177/1758835920940928

Key clinical point: Despite showing efficacy in specific subgroups, immune checkpoint inhibitors (ICIs) are unlikely to benefit most women with metastatic breast cancer.

Major finding: Objective response rates were 19% overall, 27% in PD-L1 positive patients, 18% in PD-L1 negative patients, 28% in HER2-positive breast cancer, 23% in triple-negative breast cancer, 35% when used in the first line, 26% when combined with systemic therapy, and 9% when used as monotherapy.

Study details: Meta-analysis of 27 studies of metastatic breast cancer (1,746 patients).

Disclosures: The study was funded by the National Natural Science Foundation of China. The investigators reported having no conflicts.

Citation: Zou Y et al. Ther Adv Med Oncol. 2020 Aug 17. doi: 10.1177/1758835920940928

Key clinical point: Later uptake and less extensive use of screening mammography might explain a relatively high rate of deaths from breast cancer among older women in Germany.

Major finding: Women aged ≥ 70 years in Germany had a 19% lower incidence but a 45% higher rate of mortality from breast cancer compared with their peers in the United States.

Study details: Population-based study of the Surveillance, Epidemiology, and End Results (SEER) 9 registry in the United States, and the Saarland Cancer Registry and the German Centre for Cancer Registry Data in Germany. 

Disclosures: German Cancer Aid funded the study. The investigators reported having no conflicts.

Citation: Jansen L et al. Cancers (Basel). 2020 Aug 26. doi: 10.3390/cancers12092419

Key clinical point: Later uptake and less extensive use of screening mammography might explain a relatively high rate of deaths from breast cancer among older women in Germany.

Major finding: Women aged ≥ 70 years in Germany had a 19% lower incidence but a 45% higher rate of mortality from breast cancer compared with their peers in the United States.

Study details: Population-based study of the Surveillance, Epidemiology, and End Results (SEER) 9 registry in the United States, and the Saarland Cancer Registry and the German Centre for Cancer Registry Data in Germany. 

Disclosures: German Cancer Aid funded the study. The investigators reported having no conflicts.

Citation: Jansen L et al. Cancers (Basel). 2020 Aug 26. doi: 10.3390/cancers12092419

Key clinical point: Later uptake and less extensive use of screening mammography might explain a relatively high rate of deaths from breast cancer among older women in Germany.

Major finding: Women aged ≥ 70 years in Germany had a 19% lower incidence but a 45% higher rate of mortality from breast cancer compared with their peers in the United States.

Study details: Population-based study of the Surveillance, Epidemiology, and End Results (SEER) 9 registry in the United States, and the Saarland Cancer Registry and the German Centre for Cancer Registry Data in Germany. 

Disclosures: German Cancer Aid funded the study. The investigators reported having no conflicts.

Citation: Jansen L et al. Cancers (Basel). 2020 Aug 26. doi: 10.3390/cancers12092419

Key clinical point: Prior use of conjugated equine estrogen in women who had a hysterectomy was associated with lower breast cancer incidence and mortality.

Major finding: Conjugated equine estrogen alone was associated with lower mortality (30 deaths, annualized mortality rate 0.031%), compared with placebo (46 deaths, annualized mortality rate 0.046%); HR 0.60; 95% CI, 0.37-0.97; P = .04.

Study details: This was a long-term follow-up study of two Women’s Health Initiative clinical trials of postmenopausal women with no prior breast cancer.

Disclosures: The Women’s Health Initiative is supported by the National Heart, Lung, and Blood Institute, the National Institutes of Health, and the Department of Health and Human Services. The authors reported numerous potential conflicts of interest, including receiving personal fees and grants from various government organizations, foundations, and pharmaceutical companies.

Citation: Chlebowski RT et al. JAMA. 2020 Jul 28. doi: 10.1001/jama.2020.9482.

Key clinical point: Prior use of conjugated equine estrogen in women who had a hysterectomy was associated with lower breast cancer incidence and mortality.

Major finding: Conjugated equine estrogen alone was associated with lower mortality (30 deaths, annualized mortality rate 0.031%), compared with placebo (46 deaths, annualized mortality rate 0.046%); HR 0.60; 95% CI, 0.37-0.97; P = .04.

Study details: This was a long-term follow-up study of two Women’s Health Initiative clinical trials of postmenopausal women with no prior breast cancer.

Disclosures: The Women’s Health Initiative is supported by the National Heart, Lung, and Blood Institute, the National Institutes of Health, and the Department of Health and Human Services. The authors reported numerous potential conflicts of interest, including receiving personal fees and grants from various government organizations, foundations, and pharmaceutical companies.

Citation: Chlebowski RT et al. JAMA. 2020 Jul 28. doi: 10.1001/jama.2020.9482.

Key clinical point: Prior use of conjugated equine estrogen in women who had a hysterectomy was associated with lower breast cancer incidence and mortality.

Major finding: Conjugated equine estrogen alone was associated with lower mortality (30 deaths, annualized mortality rate 0.031%), compared with placebo (46 deaths, annualized mortality rate 0.046%); HR 0.60; 95% CI, 0.37-0.97; P = .04.

Study details: This was a long-term follow-up study of two Women’s Health Initiative clinical trials of postmenopausal women with no prior breast cancer.

Disclosures: The Women’s Health Initiative is supported by the National Heart, Lung, and Blood Institute, the National Institutes of Health, and the Department of Health and Human Services. The authors reported numerous potential conflicts of interest, including receiving personal fees and grants from various government organizations, foundations, and pharmaceutical companies.

Citation: Chlebowski RT et al. JAMA. 2020 Jul 28. doi: 10.1001/jama.2020.9482.

Key clinical point: An automated image cytometry system called CytoPAN provides rapid cancer profiling and requires “scant” cellular specimens, according to researchers.

Major finding: In preclinical experiments, CytoPAN detected cancer in 1 hour using as few as 50 cells. In a patient cohort, CytoPAN detected breast cancer with 100% accuracy.

Study details: Preclinical research and a prospective study of 68 breast cancer patients.

Disclosures: The authors received funding from the National Institutes of Health, the MGH Scholar Fund, and Robert Wood Johnson Foundation. Some authors disclosed relationships with Akili, Accure Health, ModeRNA, Tarveda, Lumicell, and Noul.

Citation: Min J et al. Sci Transl Med. 2020 Aug 5. doi: 10.1126/scitranslmed.aaz9746.

Key clinical point: An automated image cytometry system called CytoPAN provides rapid cancer profiling and requires “scant” cellular specimens, according to researchers.

Major finding: In preclinical experiments, CytoPAN detected cancer in 1 hour using as few as 50 cells. In a patient cohort, CytoPAN detected breast cancer with 100% accuracy.

Study details: Preclinical research and a prospective study of 68 breast cancer patients.

Disclosures: The authors received funding from the National Institutes of Health, the MGH Scholar Fund, and Robert Wood Johnson Foundation. Some authors disclosed relationships with Akili, Accure Health, ModeRNA, Tarveda, Lumicell, and Noul.

Citation: Min J et al. Sci Transl Med. 2020 Aug 5. doi: 10.1126/scitranslmed.aaz9746.

Key clinical point: An automated image cytometry system called CytoPAN provides rapid cancer profiling and requires “scant” cellular specimens, according to researchers.

Major finding: In preclinical experiments, CytoPAN detected cancer in 1 hour using as few as 50 cells. In a patient cohort, CytoPAN detected breast cancer with 100% accuracy.

Study details: Preclinical research and a prospective study of 68 breast cancer patients.

Disclosures: The authors received funding from the National Institutes of Health, the MGH Scholar Fund, and Robert Wood Johnson Foundation. Some authors disclosed relationships with Akili, Accure Health, ModeRNA, Tarveda, Lumicell, and Noul.

Citation: Min J et al. Sci Transl Med. 2020 Aug 5. doi: 10.1126/scitranslmed.aaz9746.

An international survey provides new insights into how COVID-19 has affected, and may continue to affect, the field of oncology.

The survey showed that “COVID-19 has had a major impact on the organization of patient care, on the well-being of caregivers, on continued medical education, and on clinical trial activities in oncology,” stated Guy Jerusalem, MD, PhD, of Centre Hospitalier Universitaire de Liège (Belgium).

Dr. Jerusalem presented these findings at the European Society for Medical Oncology Virtual Congress 2020.

The survey was distributed by 20 oncologists from 10 of the countries most affected by COVID-19. Responses were obtained from 109 oncologists representing centers in 18 countries. The responses were recorded between June 17 and July 14, 2020.

The survey consisted of 95 items intended to evaluate the impact of COVID-19 on the organization of oncologic care. Questions encompassed the capacity and service offered at each center, the magnitude of COVID-19–based care interruptions and the reasons for them, the ensuing challenges faced, interventions implemented, and the estimated harms to patients during the pandemic.

The 109 oncologists surveyed had a median of 20 years of oncology experience. A majority of respondents were men (61.5%), and the median age was 48.5 years.

The respondents had worked predominantly (62.4%) at academic hospitals, with 29.6% at community hospitals. Most respondents worked at general hospitals with an oncology unit (66.1%) rather than a specialized separate cancer center (32.1%).

The most common specialty was breast cancer (60.6%), followed by gastrointestinal cancer (10.1%), urogenital cancer (9.2%), and lung cancer (8.3%).
 

Impact on treatment

The treatment modalities affected by the pandemic – through cancellations or delays in more than 10% of patients – included surgery (in 34% of centers), chemotherapy (22%), radiotherapy (13.7%), checkpoint inhibitor therapy (9.1%), monoclonal antibodies (9%), and oral targeted therapy (3.7%).

Among oncologists treating breast cancer, cancellations/delays in more than 10% of patients were reported for everolimus (18%), CDK4/6 inhibitors (8.9%), and endocrine therapy (2.2%).

Overall, 34.8% of respondents reported increased use of granulocyte colony–stimulating factor, and 6.4% reported increased use of erythropoietin.

On the other hand, 11.1% of respondents reported a decrease in the use of double immunotherapy, and 21.9% reported decreased use of corticosteroids.

Not only can the immunosuppressive effects of steroid use increase infection risks, Dr. Jerusalem noted, fever suppression can lead to a delayed diagnosis of COVID-19.

“To circumvent potential higher infection risks or greater disease severity, we use lower doses of steroids, but this is not based on studies,” he said.

“Previous exposure to steroids or being on steroids at the time of COVID-19 infection is a detrimental factor for complications and mortality,” commented ESMO President Solange Peters, MD, PhD, of Centre Hospitalier Universitaire Vaudois in Lausanne, Switzerland.

Frontline Medical News

Dr. Peters noted that the observation was based on lung cancer registry findings. Furthermore, because data from smaller outbreaks of other coronavirus infections suggested worse prognosis and increased mortality, steroid use was already feared in the very early days of the COVID-19 pandemic.

Lastly, earlier cessation of palliative treatment was observed in 32.1% of centers, and 64.2% of respondents agreed that undertreatment because of COVID-19 is a major concern.

Dr. Jerusalem noted that the survey data do not explain the early cessation of palliative treatment. “I suspect that many patients died at home rather than alone in institutions because it was the only way they could die with their families around them.”
 

Telehealth, meetings, and trials

The survey also revealed rationales for the use of teleconsultation, including follow-up (94.5%), oral therapy (92.7%), immunotherapy (57.8%), and chemotherapy (55%).

Most respondents reported more frequent use of virtual meetings for continuing medical education (94%), oncologic team meetings (92%), and tumor boards (82%).

While about 82% of respondents said they were likely to continue the use of telemedicine, 45% said virtual conferences are not an acceptable alternative to live international conferences such as ESMO, Dr. Jerusalem said.

Finally, nearly three-quarters of respondents (72.5%) said all clinical trial activities are or will soon be activated, or never stopped, at their centers. On the other hand, 27.5% of respondents reported that their centers had major protocol violations or deviations, and 37% of respondents said they expect significant reductions in clinical trial activities this year.

Dr. Jerusalem concluded that COVID-19 is having a major, long-term impact on the organization of patient care, caregivers, continued medical education, and clinical trial activities in oncology.

He cautioned that “the risk of a delayed diagnosis of new cancers and economic consequences of COVID-19 on access to health care and cancer treatments have to be carefully evaluated.”

This research was funded by Fondation Léon Fredericq. Dr. Jerusalem disclosed relationships with Novartis, Roche, Lilly, Pfizer, Amgen, Bristol-Myers Squibb, AstraZeneca, Daiichi Sankyo, AbbVie, MedImmune, and Merck. Dr. Peters disclosed relationships with AbbVie, Amgen, AstraZeneca, and many other companies.

SOURCE: Jerusalem G et al. ESMO 2020, Abstract LBA76.

An international survey provides new insights into how COVID-19 has affected, and may continue to affect, the field of oncology.

The survey showed that “COVID-19 has had a major impact on the organization of patient care, on the well-being of caregivers, on continued medical education, and on clinical trial activities in oncology,” stated Guy Jerusalem, MD, PhD, of Centre Hospitalier Universitaire de Liège (Belgium).

Dr. Jerusalem presented these findings at the European Society for Medical Oncology Virtual Congress 2020.

The survey was distributed by 20 oncologists from 10 of the countries most affected by COVID-19. Responses were obtained from 109 oncologists representing centers in 18 countries. The responses were recorded between June 17 and July 14, 2020.

The survey consisted of 95 items intended to evaluate the impact of COVID-19 on the organization of oncologic care. Questions encompassed the capacity and service offered at each center, the magnitude of COVID-19–based care interruptions and the reasons for them, the ensuing challenges faced, interventions implemented, and the estimated harms to patients during the pandemic.

The 109 oncologists surveyed had a median of 20 years of oncology experience. A majority of respondents were men (61.5%), and the median age was 48.5 years.

The respondents had worked predominantly (62.4%) at academic hospitals, with 29.6% at community hospitals. Most respondents worked at general hospitals with an oncology unit (66.1%) rather than a specialized separate cancer center (32.1%).

The most common specialty was breast cancer (60.6%), followed by gastrointestinal cancer (10.1%), urogenital cancer (9.2%), and lung cancer (8.3%).
 

Impact on treatment

The treatment modalities affected by the pandemic – through cancellations or delays in more than 10% of patients – included surgery (in 34% of centers), chemotherapy (22%), radiotherapy (13.7%), checkpoint inhibitor therapy (9.1%), monoclonal antibodies (9%), and oral targeted therapy (3.7%).

Among oncologists treating breast cancer, cancellations/delays in more than 10% of patients were reported for everolimus (18%), CDK4/6 inhibitors (8.9%), and endocrine therapy (2.2%).

Overall, 34.8% of respondents reported increased use of granulocyte colony–stimulating factor, and 6.4% reported increased use of erythropoietin.

On the other hand, 11.1% of respondents reported a decrease in the use of double immunotherapy, and 21.9% reported decreased use of corticosteroids.

Not only can the immunosuppressive effects of steroid use increase infection risks, Dr. Jerusalem noted, fever suppression can lead to a delayed diagnosis of COVID-19.

“To circumvent potential higher infection risks or greater disease severity, we use lower doses of steroids, but this is not based on studies,” he said.

“Previous exposure to steroids or being on steroids at the time of COVID-19 infection is a detrimental factor for complications and mortality,” commented ESMO President Solange Peters, MD, PhD, of Centre Hospitalier Universitaire Vaudois in Lausanne, Switzerland.

Frontline Medical News

Dr. Peters noted that the observation was based on lung cancer registry findings. Furthermore, because data from smaller outbreaks of other coronavirus infections suggested worse prognosis and increased mortality, steroid use was already feared in the very early days of the COVID-19 pandemic.

Lastly, earlier cessation of palliative treatment was observed in 32.1% of centers, and 64.2% of respondents agreed that undertreatment because of COVID-19 is a major concern.

Dr. Jerusalem noted that the survey data do not explain the early cessation of palliative treatment. “I suspect that many patients died at home rather than alone in institutions because it was the only way they could die with their families around them.”
 

Telehealth, meetings, and trials

The survey also revealed rationales for the use of teleconsultation, including follow-up (94.5%), oral therapy (92.7%), immunotherapy (57.8%), and chemotherapy (55%).

Most respondents reported more frequent use of virtual meetings for continuing medical education (94%), oncologic team meetings (92%), and tumor boards (82%).

While about 82% of respondents said they were likely to continue the use of telemedicine, 45% said virtual conferences are not an acceptable alternative to live international conferences such as ESMO, Dr. Jerusalem said.

Finally, nearly three-quarters of respondents (72.5%) said all clinical trial activities are or will soon be activated, or never stopped, at their centers. On the other hand, 27.5% of respondents reported that their centers had major protocol violations or deviations, and 37% of respondents said they expect significant reductions in clinical trial activities this year.

Dr. Jerusalem concluded that COVID-19 is having a major, long-term impact on the organization of patient care, caregivers, continued medical education, and clinical trial activities in oncology.

He cautioned that “the risk of a delayed diagnosis of new cancers and economic consequences of COVID-19 on access to health care and cancer treatments have to be carefully evaluated.”

This research was funded by Fondation Léon Fredericq. Dr. Jerusalem disclosed relationships with Novartis, Roche, Lilly, Pfizer, Amgen, Bristol-Myers Squibb, AstraZeneca, Daiichi Sankyo, AbbVie, MedImmune, and Merck. Dr. Peters disclosed relationships with AbbVie, Amgen, AstraZeneca, and many other companies.

SOURCE: Jerusalem G et al. ESMO 2020, Abstract LBA76.

An international survey provides new insights into how COVID-19 has affected, and may continue to affect, the field of oncology.

The survey showed that “COVID-19 has had a major impact on the organization of patient care, on the well-being of caregivers, on continued medical education, and on clinical trial activities in oncology,” stated Guy Jerusalem, MD, PhD, of Centre Hospitalier Universitaire de Liège (Belgium).

Dr. Jerusalem presented these findings at the European Society for Medical Oncology Virtual Congress 2020.

The survey was distributed by 20 oncologists from 10 of the countries most affected by COVID-19. Responses were obtained from 109 oncologists representing centers in 18 countries. The responses were recorded between June 17 and July 14, 2020.

The survey consisted of 95 items intended to evaluate the impact of COVID-19 on the organization of oncologic care. Questions encompassed the capacity and service offered at each center, the magnitude of COVID-19–based care interruptions and the reasons for them, the ensuing challenges faced, interventions implemented, and the estimated harms to patients during the pandemic.

The 109 oncologists surveyed had a median of 20 years of oncology experience. A majority of respondents were men (61.5%), and the median age was 48.5 years.

The respondents had worked predominantly (62.4%) at academic hospitals, with 29.6% at community hospitals. Most respondents worked at general hospitals with an oncology unit (66.1%) rather than a specialized separate cancer center (32.1%).

The most common specialty was breast cancer (60.6%), followed by gastrointestinal cancer (10.1%), urogenital cancer (9.2%), and lung cancer (8.3%).
 

Impact on treatment

The treatment modalities affected by the pandemic – through cancellations or delays in more than 10% of patients – included surgery (in 34% of centers), chemotherapy (22%), radiotherapy (13.7%), checkpoint inhibitor therapy (9.1%), monoclonal antibodies (9%), and oral targeted therapy (3.7%).

Among oncologists treating breast cancer, cancellations/delays in more than 10% of patients were reported for everolimus (18%), CDK4/6 inhibitors (8.9%), and endocrine therapy (2.2%).

Overall, 34.8% of respondents reported increased use of granulocyte colony–stimulating factor, and 6.4% reported increased use of erythropoietin.

On the other hand, 11.1% of respondents reported a decrease in the use of double immunotherapy, and 21.9% reported decreased use of corticosteroids.

Not only can the immunosuppressive effects of steroid use increase infection risks, Dr. Jerusalem noted, fever suppression can lead to a delayed diagnosis of COVID-19.

“To circumvent potential higher infection risks or greater disease severity, we use lower doses of steroids, but this is not based on studies,” he said.

“Previous exposure to steroids or being on steroids at the time of COVID-19 infection is a detrimental factor for complications and mortality,” commented ESMO President Solange Peters, MD, PhD, of Centre Hospitalier Universitaire Vaudois in Lausanne, Switzerland.

Frontline Medical News

Dr. Peters noted that the observation was based on lung cancer registry findings. Furthermore, because data from smaller outbreaks of other coronavirus infections suggested worse prognosis and increased mortality, steroid use was already feared in the very early days of the COVID-19 pandemic.

Lastly, earlier cessation of palliative treatment was observed in 32.1% of centers, and 64.2% of respondents agreed that undertreatment because of COVID-19 is a major concern.

Dr. Jerusalem noted that the survey data do not explain the early cessation of palliative treatment. “I suspect that many patients died at home rather than alone in institutions because it was the only way they could die with their families around them.”
 

Telehealth, meetings, and trials

The survey also revealed rationales for the use of teleconsultation, including follow-up (94.5%), oral therapy (92.7%), immunotherapy (57.8%), and chemotherapy (55%).

Most respondents reported more frequent use of virtual meetings for continuing medical education (94%), oncologic team meetings (92%), and tumor boards (82%).

While about 82% of respondents said they were likely to continue the use of telemedicine, 45% said virtual conferences are not an acceptable alternative to live international conferences such as ESMO, Dr. Jerusalem said.

Finally, nearly three-quarters of respondents (72.5%) said all clinical trial activities are or will soon be activated, or never stopped, at their centers. On the other hand, 27.5% of respondents reported that their centers had major protocol violations or deviations, and 37% of respondents said they expect significant reductions in clinical trial activities this year.

Dr. Jerusalem concluded that COVID-19 is having a major, long-term impact on the organization of patient care, caregivers, continued medical education, and clinical trial activities in oncology.

He cautioned that “the risk of a delayed diagnosis of new cancers and economic consequences of COVID-19 on access to health care and cancer treatments have to be carefully evaluated.”

This research was funded by Fondation Léon Fredericq. Dr. Jerusalem disclosed relationships with Novartis, Roche, Lilly, Pfizer, Amgen, Bristol-Myers Squibb, AstraZeneca, Daiichi Sankyo, AbbVie, MedImmune, and Merck. Dr. Peters disclosed relationships with AbbVie, Amgen, AstraZeneca, and many other companies.

SOURCE: Jerusalem G et al. ESMO 2020, Abstract LBA76.

An artificial intelligence (AI) computer algorithm performed on par with, and in some cases exceeded, radiologists in reading mammograms in a case-control study of 8,805 women undergoing routine screening.

The algorithm – from the company Lunit, which was not involved in the study – had an area under the curve of 0.956 for detection of pathologically confirmed breast cancer.

When operating at a specificity of 96.6%, the sensitivity was 81.9% for the algorithm, 77.4% for first-reader radiologists, and 80.1% for second-reader radiologists. Combining the algorithm with first-reader radiologists identified more cases than combining first- and second-reader radiologists.

These findings were published in JAMA Oncology.

The study’s authors wrote that the algorithm results are a “considerable” achievement because, unlike the radiologists, the algorithm had no access to prior mammograms or information about hormonal medications or breast symptoms.

“We believe that the time has come to evaluate AI CAD [computer-aided detection] algorithms as independent readers in prospective clinical studies,” Mattie Salim, MD, of Karolinska Institute/Karolinska University Hospital in Stockholm, and colleagues wrote.

“The authors are to be commended for providing data that support this next critical phase of discovery,” Constance Dobbins Lehman, MD, PhD, of Massachusetts General Hospital and Harvard Medical School, both in Boston, wrote in a related editorial. She added that “it is time to move beyond simulation and reader studies and enter the critical phase of rigorous, prospective clinical evaluation.”
 

Study rationale and details

Routine mammograms save lives, but the workload for radiologists is high, and the quality of assessments varies widely, Dr. Salim and colleagues wrote. There are also problems with access in areas with few radiologists.

To address these issues, academic and commercial researchers have worked hard to apply AI – specifically, deep neural networks – to computer programs that read mammograms.

For this study, the investigators conducted the first third-party external validation of three competing algorithms. The three algorithms were not named in the report, but Lunit announced that its algorithm was the best-performing algorithm after the study was published. The other two algorithms did not perform as well and remain anonymous.

The investigators compared the algorithms’ assessments with the original radiology reports for 739 women who were diagnosed with breast cancer within 12 months of their mammogram and 8,066 women with negative mammograms who remained cancer free at a 2-year follow-up.

The women, aged 40-74 years, had conventional two-dimensional imaging read by two radiologists at the Karolinska University Hospital during 2008-2015. The subjects’ median age at screening was 54.5 years.

The algorithms gave a prediction score between 0 and 1 for each breast, with 1 denoting the highest level of cancer suspicion. To enable a comparison with the binary decisions of the radiologists, the output of each algorithm was dichotomized (normal or abnormal) at a cut point defined by the mean specificity of the first-reader radiologists, 96.6%.

At a specificity of 96.6%, the sensitivity was 81.9% for the Lunit algorithm, 67.0% for one anonymous algorithm (AI-2), 67.4% for the other anonymous algorithm (AI-3), 77.4% for first-reader radiologists, and 80.1% for second-reader radiologists

The investigators also ran their analysis at a cut point of 88.9% specificity. The sensitivity was 88.6% for the Lunit algorithm, 80.0% for AI-2, and 80.2% for AI-3.

“This can be compared with the Breast Cancer Surveillance Consortium benchmarks of 86.9% sensitivity at 88.9% specificity,” the authors wrote.

The most potent screening strategy was combining the Lunit algorithm with the first reader, which increased cancer detection by 8% but came at the cost of a 77% increase in abnormal assessments.

“More true-positive cases would likely be found, but a much larger proportion of false-positive examinations would have to be handled in the ensuing consensus discussion,” the authors wrote. “[A] cost-benefit analysis is required ... to determine the economic implications of adding a human reader at all.”

The team noted that the Lunit algorithm was trained on images of South Korean women from GE equipment.

“Although we do not have ethnic descriptors of our study population, the vast majority of women in Stockholm are White, and all images in our study were acquired on Hologic equipment,” the authors wrote. “In training AI algorithms for mammographic cancer detection, matching ethnic and equipment distributions between the training population and the clinical test population may not be of highest importance.”

As for why the Lunit algorithm outperformed the other two algorithms, one explanation may be that the Lunit algorithm was trained on more mammograms – 72,000 cancer and 680,000 normal images (vs. 10,000 cancer and 229,000 normal images for AI-2; 6,000 cancer and 106,000 normal images for AI-3).

As for next steps, the investigators are planning a prospective clinical study to see how AI works as an independent reviewer of mammograms in a day-to-day clinical environment, both as a third reviewer and to help select women for follow-up MRI.

The current study was funded by the Stockholm County Council. The investigators disclosed financial relationships with the Swedish Research Council, the Swedish Cancer Society, Stockholm City Council, Collective Minds Radiology, and Pfizer. Dr Lehman’s institution receives grants from GE Healthcare.

aotto@mdedge.com

SOURCE: Salim M et al. JAMA Oncol. 2020 Aug 27. doi: 10.1001/jamaoncol.2020.3321.

An artificial intelligence (AI) computer algorithm performed on par with, and in some cases exceeded, radiologists in reading mammograms in a case-control study of 8,805 women undergoing routine screening.

The algorithm – from the company Lunit, which was not involved in the study – had an area under the curve of 0.956 for detection of pathologically confirmed breast cancer.

When operating at a specificity of 96.6%, the sensitivity was 81.9% for the algorithm, 77.4% for first-reader radiologists, and 80.1% for second-reader radiologists. Combining the algorithm with first-reader radiologists identified more cases than combining first- and second-reader radiologists.

These findings were published in JAMA Oncology.

The study’s authors wrote that the algorithm results are a “considerable” achievement because, unlike the radiologists, the algorithm had no access to prior mammograms or information about hormonal medications or breast symptoms.

“We believe that the time has come to evaluate AI CAD [computer-aided detection] algorithms as independent readers in prospective clinical studies,” Mattie Salim, MD, of Karolinska Institute/Karolinska University Hospital in Stockholm, and colleagues wrote.

“The authors are to be commended for providing data that support this next critical phase of discovery,” Constance Dobbins Lehman, MD, PhD, of Massachusetts General Hospital and Harvard Medical School, both in Boston, wrote in a related editorial. She added that “it is time to move beyond simulation and reader studies and enter the critical phase of rigorous, prospective clinical evaluation.”
 

Study rationale and details

Routine mammograms save lives, but the workload for radiologists is high, and the quality of assessments varies widely, Dr. Salim and colleagues wrote. There are also problems with access in areas with few radiologists.

To address these issues, academic and commercial researchers have worked hard to apply AI – specifically, deep neural networks – to computer programs that read mammograms.

For this study, the investigators conducted the first third-party external validation of three competing algorithms. The three algorithms were not named in the report, but Lunit announced that its algorithm was the best-performing algorithm after the study was published. The other two algorithms did not perform as well and remain anonymous.

The investigators compared the algorithms’ assessments with the original radiology reports for 739 women who were diagnosed with breast cancer within 12 months of their mammogram and 8,066 women with negative mammograms who remained cancer free at a 2-year follow-up.

The women, aged 40-74 years, had conventional two-dimensional imaging read by two radiologists at the Karolinska University Hospital during 2008-2015. The subjects’ median age at screening was 54.5 years.

The algorithms gave a prediction score between 0 and 1 for each breast, with 1 denoting the highest level of cancer suspicion. To enable a comparison with the binary decisions of the radiologists, the output of each algorithm was dichotomized (normal or abnormal) at a cut point defined by the mean specificity of the first-reader radiologists, 96.6%.

At a specificity of 96.6%, the sensitivity was 81.9% for the Lunit algorithm, 67.0% for one anonymous algorithm (AI-2), 67.4% for the other anonymous algorithm (AI-3), 77.4% for first-reader radiologists, and 80.1% for second-reader radiologists

The investigators also ran their analysis at a cut point of 88.9% specificity. The sensitivity was 88.6% for the Lunit algorithm, 80.0% for AI-2, and 80.2% for AI-3.

“This can be compared with the Breast Cancer Surveillance Consortium benchmarks of 86.9% sensitivity at 88.9% specificity,” the authors wrote.

The most potent screening strategy was combining the Lunit algorithm with the first reader, which increased cancer detection by 8% but came at the cost of a 77% increase in abnormal assessments.

“More true-positive cases would likely be found, but a much larger proportion of false-positive examinations would have to be handled in the ensuing consensus discussion,” the authors wrote. “[A] cost-benefit analysis is required ... to determine the economic implications of adding a human reader at all.”

The team noted that the Lunit algorithm was trained on images of South Korean women from GE equipment.

“Although we do not have ethnic descriptors of our study population, the vast majority of women in Stockholm are White, and all images in our study were acquired on Hologic equipment,” the authors wrote. “In training AI algorithms for mammographic cancer detection, matching ethnic and equipment distributions between the training population and the clinical test population may not be of highest importance.”

As for why the Lunit algorithm outperformed the other two algorithms, one explanation may be that the Lunit algorithm was trained on more mammograms – 72,000 cancer and 680,000 normal images (vs. 10,000 cancer and 229,000 normal images for AI-2; 6,000 cancer and 106,000 normal images for AI-3).

As for next steps, the investigators are planning a prospective clinical study to see how AI works as an independent reviewer of mammograms in a day-to-day clinical environment, both as a third reviewer and to help select women for follow-up MRI.

The current study was funded by the Stockholm County Council. The investigators disclosed financial relationships with the Swedish Research Council, the Swedish Cancer Society, Stockholm City Council, Collective Minds Radiology, and Pfizer. Dr Lehman’s institution receives grants from GE Healthcare.

aotto@mdedge.com

SOURCE: Salim M et al. JAMA Oncol. 2020 Aug 27. doi: 10.1001/jamaoncol.2020.3321.

An artificial intelligence (AI) computer algorithm performed on par with, and in some cases exceeded, radiologists in reading mammograms in a case-control study of 8,805 women undergoing routine screening.

The algorithm – from the company Lunit, which was not involved in the study – had an area under the curve of 0.956 for detection of pathologically confirmed breast cancer.

When operating at a specificity of 96.6%, the sensitivity was 81.9% for the algorithm, 77.4% for first-reader radiologists, and 80.1% for second-reader radiologists. Combining the algorithm with first-reader radiologists identified more cases than combining first- and second-reader radiologists.

These findings were published in JAMA Oncology.

The study’s authors wrote that the algorithm results are a “considerable” achievement because, unlike the radiologists, the algorithm had no access to prior mammograms or information about hormonal medications or breast symptoms.

“We believe that the time has come to evaluate AI CAD [computer-aided detection] algorithms as independent readers in prospective clinical studies,” Mattie Salim, MD, of Karolinska Institute/Karolinska University Hospital in Stockholm, and colleagues wrote.

“The authors are to be commended for providing data that support this next critical phase of discovery,” Constance Dobbins Lehman, MD, PhD, of Massachusetts General Hospital and Harvard Medical School, both in Boston, wrote in a related editorial. She added that “it is time to move beyond simulation and reader studies and enter the critical phase of rigorous, prospective clinical evaluation.”
 

Study rationale and details

Routine mammograms save lives, but the workload for radiologists is high, and the quality of assessments varies widely, Dr. Salim and colleagues wrote. There are also problems with access in areas with few radiologists.

To address these issues, academic and commercial researchers have worked hard to apply AI – specifically, deep neural networks – to computer programs that read mammograms.

For this study, the investigators conducted the first third-party external validation of three competing algorithms. The three algorithms were not named in the report, but Lunit announced that its algorithm was the best-performing algorithm after the study was published. The other two algorithms did not perform as well and remain anonymous.

The investigators compared the algorithms’ assessments with the original radiology reports for 739 women who were diagnosed with breast cancer within 12 months of their mammogram and 8,066 women with negative mammograms who remained cancer free at a 2-year follow-up.

The women, aged 40-74 years, had conventional two-dimensional imaging read by two radiologists at the Karolinska University Hospital during 2008-2015. The subjects’ median age at screening was 54.5 years.

The algorithms gave a prediction score between 0 and 1 for each breast, with 1 denoting the highest level of cancer suspicion. To enable a comparison with the binary decisions of the radiologists, the output of each algorithm was dichotomized (normal or abnormal) at a cut point defined by the mean specificity of the first-reader radiologists, 96.6%.

At a specificity of 96.6%, the sensitivity was 81.9% for the Lunit algorithm, 67.0% for one anonymous algorithm (AI-2), 67.4% for the other anonymous algorithm (AI-3), 77.4% for first-reader radiologists, and 80.1% for second-reader radiologists

The investigators also ran their analysis at a cut point of 88.9% specificity. The sensitivity was 88.6% for the Lunit algorithm, 80.0% for AI-2, and 80.2% for AI-3.

“This can be compared with the Breast Cancer Surveillance Consortium benchmarks of 86.9% sensitivity at 88.9% specificity,” the authors wrote.

The most potent screening strategy was combining the Lunit algorithm with the first reader, which increased cancer detection by 8% but came at the cost of a 77% increase in abnormal assessments.

“More true-positive cases would likely be found, but a much larger proportion of false-positive examinations would have to be handled in the ensuing consensus discussion,” the authors wrote. “[A] cost-benefit analysis is required ... to determine the economic implications of adding a human reader at all.”

The team noted that the Lunit algorithm was trained on images of South Korean women from GE equipment.

“Although we do not have ethnic descriptors of our study population, the vast majority of women in Stockholm are White, and all images in our study were acquired on Hologic equipment,” the authors wrote. “In training AI algorithms for mammographic cancer detection, matching ethnic and equipment distributions between the training population and the clinical test population may not be of highest importance.”

As for why the Lunit algorithm outperformed the other two algorithms, one explanation may be that the Lunit algorithm was trained on more mammograms – 72,000 cancer and 680,000 normal images (vs. 10,000 cancer and 229,000 normal images for AI-2; 6,000 cancer and 106,000 normal images for AI-3).

As for next steps, the investigators are planning a prospective clinical study to see how AI works as an independent reviewer of mammograms in a day-to-day clinical environment, both as a third reviewer and to help select women for follow-up MRI.

The current study was funded by the Stockholm County Council. The investigators disclosed financial relationships with the Swedish Research Council, the Swedish Cancer Society, Stockholm City Council, Collective Minds Radiology, and Pfizer. Dr Lehman’s institution receives grants from GE Healthcare.

aotto@mdedge.com

SOURCE: Salim M et al. JAMA Oncol. 2020 Aug 27. doi: 10.1001/jamaoncol.2020.3321.