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A test that uses machine learning may improve the management of patients with pancreatic cysts, sparing some of them unnecessary surgery, a cohort study suggests.
The test, called CompCyst, integrates clinical, imaging, and biomarker data. It proved more accurate than the current standard of care for correctly determining whether patients should be discharged from follow-up, immediately operated on, or monitored.
Rachel Karchin, PhD, of the Johns Hopkins Whiting School of Engineering in Baltimore, reported these results at the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging (Abstract IA-13).
“Preoperative diagnosis of pancreatic cysts and managing patients who present with a cyst are a clinical conundrum because pancreatic cancer is so deadly, while the decision to surgically resect a cyst is complicated by the danger of the surgery, which has high morbidity and mortality,” Dr. Karchin explained. “The challenge of the diagnostic test is to place patients into one of three groups: those who should be discharged, who should be operated on, and who should be monitored.”
High sensitivity is important for the operate and monitor groups to ensure identification of all patients needing these approaches, whereas higher specificity is important for the discharge group to avoid falsely classifying premalignant cysts, Dr. Karchin said.
She and her colleagues applied machine learning to this classification challenge, using data from 862 patients who had undergone resection of pancreatic cysts at 16 centers in the United States, Europe, and Asia. All patients had a known cyst histopathology, which served as the gold standard, and a known clinical management strategy (discharge, operate, or monitor).
The investigators used a multivariate organization of combinatorial alterations algorithm that integrates clinical features, imaging characteristics, cyst fluid genetics, and serum biomarkers to create classifiers. This algorithm can be trained to maximize sensitivity, maximize specificity, or balance these metrics, Dr. Karchin noted.
The resulting test, CompCyst, was trained using data from 436 of the patients and then validated in the remaining 426 patients.
In the validation cohort, for classifying patients who should be discharged from care, the test had a sensitivity of 46% and a specificity of 100%, according to results reported at the conference and published previously (Sci Transl Med. 2019 Jul 19. doi: 10.1126/scitranslmed.aav4772).
For immediately operating, CompCyst had a sensitivity of 91% and a specificity of 54%. And for monitoring the patient, the test had a sensitivity of 99% and a specificity of 30%.
When CompCyst was compared against the standard of care based on conventional clinical and imaging criteria alone, the former was more accurate. CompCyst correctly identified larger shares of patients who should have been discharged (60% vs. 19%) and who should have been monitored (49% vs. 34%), and the test identified a similar share of patients who should have immediately had an operation (91% vs. 89%).
“The takeaway from this is that standard of care is sending too many patients unnecessarily to surgery,” Dr. Karchin commented. “The CompCyst test, with application of the three classifiers sequentially – discharge, operate, or monitor – could reduce unnecessary surgery by 60% or more based on our calculations.”
“While our study was retrospective, it shows promising results in reducing unnecessary surgeries, compared to current standard of care,” she said, adding that a prospective study is planned next.
“In 10-12 weeks, this CompCyst diagnostic test is going to be available at Johns Hopkins for patients. I’m very excited about that,” Dr. Karchin concluded. “We hope that our study shows the potential of combining clinical, imaging, and genetic features with machine learning to improve clinical judgment about many diseases.”
Dr. Karchin disclosed no conflicts of interest. The study was supported by the Lustgarten Foundation for Pancreatic Cancer Research, the Virginia and D.K. Ludwig Fund for Cancer Research, the Sol Goldman Pancreatic Cancer Research Center, the Michael Rolfe Pancreatic Cancer Research Foundation, the Benjamin Baker Scholarship, and the National Institutes of Health.
A test that uses machine learning may improve the management of patients with pancreatic cysts, sparing some of them unnecessary surgery, a cohort study suggests.
The test, called CompCyst, integrates clinical, imaging, and biomarker data. It proved more accurate than the current standard of care for correctly determining whether patients should be discharged from follow-up, immediately operated on, or monitored.
Rachel Karchin, PhD, of the Johns Hopkins Whiting School of Engineering in Baltimore, reported these results at the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging (Abstract IA-13).
“Preoperative diagnosis of pancreatic cysts and managing patients who present with a cyst are a clinical conundrum because pancreatic cancer is so deadly, while the decision to surgically resect a cyst is complicated by the danger of the surgery, which has high morbidity and mortality,” Dr. Karchin explained. “The challenge of the diagnostic test is to place patients into one of three groups: those who should be discharged, who should be operated on, and who should be monitored.”
High sensitivity is important for the operate and monitor groups to ensure identification of all patients needing these approaches, whereas higher specificity is important for the discharge group to avoid falsely classifying premalignant cysts, Dr. Karchin said.
She and her colleagues applied machine learning to this classification challenge, using data from 862 patients who had undergone resection of pancreatic cysts at 16 centers in the United States, Europe, and Asia. All patients had a known cyst histopathology, which served as the gold standard, and a known clinical management strategy (discharge, operate, or monitor).
The investigators used a multivariate organization of combinatorial alterations algorithm that integrates clinical features, imaging characteristics, cyst fluid genetics, and serum biomarkers to create classifiers. This algorithm can be trained to maximize sensitivity, maximize specificity, or balance these metrics, Dr. Karchin noted.
The resulting test, CompCyst, was trained using data from 436 of the patients and then validated in the remaining 426 patients.
In the validation cohort, for classifying patients who should be discharged from care, the test had a sensitivity of 46% and a specificity of 100%, according to results reported at the conference and published previously (Sci Transl Med. 2019 Jul 19. doi: 10.1126/scitranslmed.aav4772).
For immediately operating, CompCyst had a sensitivity of 91% and a specificity of 54%. And for monitoring the patient, the test had a sensitivity of 99% and a specificity of 30%.
When CompCyst was compared against the standard of care based on conventional clinical and imaging criteria alone, the former was more accurate. CompCyst correctly identified larger shares of patients who should have been discharged (60% vs. 19%) and who should have been monitored (49% vs. 34%), and the test identified a similar share of patients who should have immediately had an operation (91% vs. 89%).
“The takeaway from this is that standard of care is sending too many patients unnecessarily to surgery,” Dr. Karchin commented. “The CompCyst test, with application of the three classifiers sequentially – discharge, operate, or monitor – could reduce unnecessary surgery by 60% or more based on our calculations.”
“While our study was retrospective, it shows promising results in reducing unnecessary surgeries, compared to current standard of care,” she said, adding that a prospective study is planned next.
“In 10-12 weeks, this CompCyst diagnostic test is going to be available at Johns Hopkins for patients. I’m very excited about that,” Dr. Karchin concluded. “We hope that our study shows the potential of combining clinical, imaging, and genetic features with machine learning to improve clinical judgment about many diseases.”
Dr. Karchin disclosed no conflicts of interest. The study was supported by the Lustgarten Foundation for Pancreatic Cancer Research, the Virginia and D.K. Ludwig Fund for Cancer Research, the Sol Goldman Pancreatic Cancer Research Center, the Michael Rolfe Pancreatic Cancer Research Foundation, the Benjamin Baker Scholarship, and the National Institutes of Health.
A test that uses machine learning may improve the management of patients with pancreatic cysts, sparing some of them unnecessary surgery, a cohort study suggests.
The test, called CompCyst, integrates clinical, imaging, and biomarker data. It proved more accurate than the current standard of care for correctly determining whether patients should be discharged from follow-up, immediately operated on, or monitored.
Rachel Karchin, PhD, of the Johns Hopkins Whiting School of Engineering in Baltimore, reported these results at the AACR Virtual Special Conference: Artificial Intelligence, Diagnosis, and Imaging (Abstract IA-13).
“Preoperative diagnosis of pancreatic cysts and managing patients who present with a cyst are a clinical conundrum because pancreatic cancer is so deadly, while the decision to surgically resect a cyst is complicated by the danger of the surgery, which has high morbidity and mortality,” Dr. Karchin explained. “The challenge of the diagnostic test is to place patients into one of three groups: those who should be discharged, who should be operated on, and who should be monitored.”
High sensitivity is important for the operate and monitor groups to ensure identification of all patients needing these approaches, whereas higher specificity is important for the discharge group to avoid falsely classifying premalignant cysts, Dr. Karchin said.
She and her colleagues applied machine learning to this classification challenge, using data from 862 patients who had undergone resection of pancreatic cysts at 16 centers in the United States, Europe, and Asia. All patients had a known cyst histopathology, which served as the gold standard, and a known clinical management strategy (discharge, operate, or monitor).
The investigators used a multivariate organization of combinatorial alterations algorithm that integrates clinical features, imaging characteristics, cyst fluid genetics, and serum biomarkers to create classifiers. This algorithm can be trained to maximize sensitivity, maximize specificity, or balance these metrics, Dr. Karchin noted.
The resulting test, CompCyst, was trained using data from 436 of the patients and then validated in the remaining 426 patients.
In the validation cohort, for classifying patients who should be discharged from care, the test had a sensitivity of 46% and a specificity of 100%, according to results reported at the conference and published previously (Sci Transl Med. 2019 Jul 19. doi: 10.1126/scitranslmed.aav4772).
For immediately operating, CompCyst had a sensitivity of 91% and a specificity of 54%. And for monitoring the patient, the test had a sensitivity of 99% and a specificity of 30%.
When CompCyst was compared against the standard of care based on conventional clinical and imaging criteria alone, the former was more accurate. CompCyst correctly identified larger shares of patients who should have been discharged (60% vs. 19%) and who should have been monitored (49% vs. 34%), and the test identified a similar share of patients who should have immediately had an operation (91% vs. 89%).
“The takeaway from this is that standard of care is sending too many patients unnecessarily to surgery,” Dr. Karchin commented. “The CompCyst test, with application of the three classifiers sequentially – discharge, operate, or monitor – could reduce unnecessary surgery by 60% or more based on our calculations.”
“While our study was retrospective, it shows promising results in reducing unnecessary surgeries, compared to current standard of care,” she said, adding that a prospective study is planned next.
“In 10-12 weeks, this CompCyst diagnostic test is going to be available at Johns Hopkins for patients. I’m very excited about that,” Dr. Karchin concluded. “We hope that our study shows the potential of combining clinical, imaging, and genetic features with machine learning to improve clinical judgment about many diseases.”
Dr. Karchin disclosed no conflicts of interest. The study was supported by the Lustgarten Foundation for Pancreatic Cancer Research, the Virginia and D.K. Ludwig Fund for Cancer Research, the Sol Goldman Pancreatic Cancer Research Center, the Michael Rolfe Pancreatic Cancer Research Foundation, the Benjamin Baker Scholarship, and the National Institutes of Health.
FROM AACR: AI, DIAGNOSIS, AND IMAGING 2021
