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Background The prevalence of non-medullary thyroid cancer (NMTC) is increasing worldwide. Although most NMTCs grow slowly, conventional therapies are less effective in advanced tumors. Approximately 5–15% of NMTCs have a significant germline genetic component. Awareness of the NMTC susceptibility genes may lead to earlier diagnosis and better cancer prevention. Objective The aim of this study was to provide the current panorama of susceptibility genes associated with NMTC and the spectrum of diseases associated with these genes. Methods Twenty-five candidate genes were identified by searching for relevant studies in PubMed. Each candidate gene was carefully checked using six authoritative genetic resources: ClinGen, National Comprehensive Cancer Network guidelines, Online Mendelian Inheritance in Man, Genetics Home Reference, GeneCards, and Gene-NCBI, and a validated natural language processing (NLP)-based literature review protocol was used to further assess gene–disease associations where there was ambiguity. Results Among 25 candidate genes, 10 (APC, DICER1, FOXE1, HABP2, NKX2-1, PRKAR1A, PTEN, SDHB, SDHD, and SRGAP1) were verified among the six genetic resources. Two additional genes, CHEK2 and SEC23B, were verified using the NLP protocol. Seventy-nine diseases were found to be associated with these 12 NMTC susceptibility genes. The following diseases were associated with more than one NMTC susceptibility gene: colorectal cancer, breast cancer, gastric cancer, kidney cancer, gastrointestinal stromal tumor, paraganglioma, pheochromocytoma, and benign skin conditions. Conclusion Twelve genes predisposing to NMTC and their associated disease spectra were identified and verified. Clinicians should be aware that patients with certain pathogenic variants may require more aggressive surveillance beyond their thyroid cancer risk.

Purpose Atypical ductal hyperplasia (ADH) significantly increases the risk of breast cancer in women. However, little is known about the implications of ADH in men. Methods Review of 932 males with breast pathology was performed to identify cases of ADH. Patients were excluded if ADH was upgraded to cancer on excision, or if they had contralateral breast cancer. Cases were reviewed to determine whether any male with ADH developed breast cancer. Results Nineteen males were diagnosed with ADH from June 2003 to September 2018. All had gynecomastia. Surgical procedure was mastectomy in 8 patients and excision/reduction in 11. One patient had their nipple areola complex removed, and 1 required a free nipple graft. Median patient age at ADH diagnosis was 25 years (range 18–72 years). Of the 14 patients with bilateral gynecomastia, 10 had bilateral ADH and 4 had unilateral. Five cases of ADH were described as severe, bordering on ductal carcinoma in situ. No patient reported a family history of breast cancer. No patient took tamoxifen. At a mean follow-up of 75 months (range 4–185 months), no patient developed breast cancer. Conclusion Our study is the first to provide follow-up information for males with ADH. With 6 years of mean follow-up, no male in our series has developed breast cancer. This suggests that either ADH in men does not pose the same risk as ADH in women or that surgical excision of symptomatic gynecomastia in men effectively reduces the risk of breast cancer.

© 2018 American Academy of Hospice and Palliative Medicine Context: Clinicians document cancer patients’ symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. Objectives: To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes. Methods: The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning. Results: The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes. Conclusion: We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications.

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Purpose: Mammoplasty removes random samples of breast tissue from asymptomatic women providing a unique method for evaluating background prevalence of breast pathology in normal population. Our goal was to identify the rate of atypical breast lesions and cancers in women of various ages in the largest mammoplasty cohort reported to date. Methods: We analyzed pathologic reports from patients undergoing bilateral mammoplasty, using natural language processing algorithm, verified by human review. Patients with a prior history of breast cancer or atypia were excluded. Results: A total of 4775 patients were deemed eligible. Median age was 40 (range 13–86) and was higher in patients with any incidental finding compared to patients with normal reports (52 vs. 39 years, p = 0.0001). Pathological findings were detected in 7.06% (337) of procedures. Benign high-risk lesions were found in 299 patients (6.26%). Invasive carcinoma and ductal carcinoma in situ were detected in 15 (0.31%) and 23 (0.48%) patients, respectively. The rate of atypias and cancers increased with age. Conclusion: The overall rate of abnormal findings in asymptomatic patients undergoing mammoplasty was 7.06%, increasing with age. As these results are based on random sample of breast tissue, they likely underestimate the prevalence of abnormal findings in asymptomatic women.

Pathogenic variants in germline cancer susceptibility genes can increase the risk of a large number of diseases. Our study aims to assess the disease spectrum of gastric cancer susceptibility genes and to develop a comprehensive resource of gene–disease associations for clinicians. Twenty-seven potential germline gastric cancer susceptibility genes were identified from three review articles and from six commonly used genetic information resources. The diseases associated with each gene were evaluated via a semi-structured review of six genetic resources and an additional literature review using a natural language processing (NLP)-based procedure. Out of 27 candidate genes, 13 were identified as gastric cancer susceptibility genes (APC, ATM, BMPR1A, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH-Biallelic, PALB2, SMAD4, and STK11). A total of 145 gene–disease associations (with 45 unique diseases) were found to be associated with these 13 genes. Other gastrointestinal cancers were prominent among identified associations, with 11 of 13 gastric cancer susceptibility genes also associated with colorectal cancer, eight genes associated with pancreatic cancer, and seven genes associated with small intestine cancer. Gastric cancer susceptibility genes are frequently associated with other diseases as well as gastric cancer, with potential implications for how carriers of these genes are screened and managed. Unfortunately, commonly used genetic resources provide heterogeneous information with regard to these genes and their associated diseases, highlighting the importance of developing guides for clinicians that integrate data across available resources and the medical literature.

© 2019 by American Society of Clinical Oncology PURPOSE The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the literature to understand the clinical implications of pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance—risk of cancer for germline mutation carriers—or prevalence of germline genetic mutations. MATERIALS AND METHODS We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated data set for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule on the basis of the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule on the basis of the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS For penetrance classification, we annotated 3,740 paper titles and abstracts and evaluated the two models using 10-fold cross-validation. The SVM model achieved 88.93% accuracy—percentage of papers that were correctly classified—whereas the CNN model achieved 88.53% accuracy. For prevalence classification, we annotated 3,753 paper titles and abstracts. The SVM model achieved 88.92% accuracy and the CNN model achieved 88.52% accuracy. CONCLUSION Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene–cancer associations and keep the knowledge bases for clinical decision support tools up to date.

Pathogenic variants in germline cancer susceptibility genes can increase the risk of a large number of diseases. Our study aims to assess the disease spectrum of gastric cancer susceptibility genes and to develop a comprehensive resource of gene–disease associations for clinicians. Twenty-seven potential germline gastric cancer susceptibility genes were identified from three review articles and from six commonly used genetic information resources. The diseases associated with each gene were evaluated via a semi-structured review of six genetic resources and an additional literature review using a natural language processing (NLP)-based procedure. Out of 27 candidate genes, 13 were identified as gastric cancer susceptibility genes (APC, ATM, BMPR1A, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH-Biallelic, PALB2, SMAD4, and STK11). A total of 145 gene–disease associations (with 45 unique diseases) were found to be associated with these 13 genes. Other gastrointestinal cancers were prominent among identified associations, with 11 of 13 gastric cancer susceptibility genes also associated with colorectal cancer, eight genes associated with pancreatic cancer, and seven genes associated with small intestine cancer. Gastric cancer susceptibility genes are frequently associated with other diseases as well as gastric cancer, with potential implications for how carriers of these genes are screened and managed. Unfortunately, commonly used genetic resources provide heterogeneous information with regard to these genes and their associated diseases, highlighting the importance of developing guides for clinicians that integrate data across available resources and the medical literature.

© 2019 by American Society of Clinical Oncology PURPOSE The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools that help to monitor and prioritize the literature to understand the clinical implications of pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance—risk of cancer for germline mutation carriers—or prevalence of germline genetic mutations. MATERIALS AND METHODS We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated data set for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule on the basis of the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule on the basis of the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS For penetrance classification, we annotated 3,740 paper titles and abstracts and evaluated the two models using 10-fold cross-validation. The SVM model achieved 88.93% accuracy—percentage of papers that were correctly classified—whereas the CNN model achieved 88.53% accuracy. For prevalence classification, we annotated 3,753 paper titles and abstracts. The SVM model achieved 88.92% accuracy and the CNN model achieved 88.52% accuracy. CONCLUSION Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene–cancer associations and keep the knowledge bases for clinical decision support tools up to date.

Purpose Atypical ductal hyperplasia (ADH) significantly increases the risk of breast cancer in women. However, little is known about the implications of ADH in men. Methods Review of 932 males with breast pathology was performed to identify cases of ADH. Patients were excluded if ADH was upgraded to cancer on excision, or if they had contralateral breast cancer. Cases were reviewed to determine whether any male with ADH developed breast cancer. Results Nineteen males were diagnosed with ADH from June 2003 to September 2018. All had gynecomastia. Surgical procedure was mastectomy in 8 patients and excision/reduction in 11. One patient had their nipple areola complex removed, and 1 required a free nipple graft. Median patient age at ADH diagnosis was 25 years (range 18–72 years). Of the 14 patients with bilateral gynecomastia, 10 had bilateral ADH and 4 had unilateral. Five cases of ADH were described as severe, bordering on ductal carcinoma in situ. No patient reported a family history of breast cancer. No patient took tamoxifen. At a mean follow-up of 75 months (range 4–185 months), no patient developed breast cancer. Conclusion Our study is the first to provide follow-up information for males with ADH. With 6 years of mean follow-up, no male in our series has developed breast cancer. This suggests that either ADH in men does not pose the same risk as ADH in women or that surgical excision of symptomatic gynecomastia in men effectively reduces the risk of breast cancer.

Background The prevalence of non-medullary thyroid cancer (NMTC) is increasing worldwide. Although most NMTCs grow slowly, conventional therapies are less effective in advanced tumors. Approximately 5–15% of NMTCs have a significant germline genetic component. Awareness of the NMTC susceptibility genes may lead to earlier diagnosis and better cancer prevention. Objective The aim of this study was to provide the current panorama of susceptibility genes associated with NMTC and the spectrum of diseases associated with these genes. Methods Twenty-five candidate genes were identified by searching for relevant studies in PubMed. Each candidate gene was carefully checked using six authoritative genetic resources: ClinGen, National Comprehensive Cancer Network guidelines, Online Mendelian Inheritance in Man, Genetics Home Reference, GeneCards, and Gene-NCBI, and a validated natural language processing (NLP)-based literature review protocol was used to further assess gene–disease associations where there was ambiguity. Results Among 25 candidate genes, 10 (APC, DICER1, FOXE1, HABP2, NKX2-1, PRKAR1A, PTEN, SDHB, SDHD, and SRGAP1) were verified among the six genetic resources. Two additional genes, CHEK2 and SEC23B, were verified using the NLP protocol. Seventy-nine diseases were found to be associated with these 12 NMTC susceptibility genes. The following diseases were associated with more than one NMTC susceptibility gene: colorectal cancer, breast cancer, gastric cancer, kidney cancer, gastrointestinal stromal tumor, paraganglioma, pheochromocytoma, and benign skin conditions. Conclusion Twelve genes predisposing to NMTC and their associated disease spectra were identified and verified. Clinicians should be aware that patients with certain pathogenic variants may require more aggressive surveillance beyond their thyroid cancer risk.

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Purpose: Mammoplasty removes random samples of breast tissue from asymptomatic women providing a unique method for evaluating background prevalence of breast pathology in normal population. Our goal was to identify the rate of atypical breast lesions and cancers in women of various ages in the largest mammoplasty cohort reported to date. Methods: We analyzed pathologic reports from patients undergoing bilateral mammoplasty, using natural language processing algorithm, verified by human review. Patients with a prior history of breast cancer or atypia were excluded. Results: A total of 4775 patients were deemed eligible. Median age was 40 (range 13–86) and was higher in patients with any incidental finding compared to patients with normal reports (52 vs. 39 years, p = 0.0001). Pathological findings were detected in 7.06% (337) of procedures. Benign high-risk lesions were found in 299 patients (6.26%). Invasive carcinoma and ductal carcinoma in situ were detected in 15 (0.31%) and 23 (0.48%) patients, respectively. The rate of atypias and cancers increased with age. Conclusion: The overall rate of abnormal findings in asymptomatic patients undergoing mammoplasty was 7.06%, increasing with age. As these results are based on random sample of breast tissue, they likely underestimate the prevalence of abnormal findings in asymptomatic women.

© 2016, Springer Science+Business Media New York. Purpose: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. Methods: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women’s Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. Results: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. Conclusions: Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.

Pathogenic variants in germline cancer susceptibility genes can increase the risk of a large number of diseases. Our study aims to assess the disease spectrum of gastric cancer susceptibility genes and to develop a comprehensive resource of gene–disease associations for clinicians. Twenty-seven potential germline gastric cancer susceptibility genes were identified from three review articles and from six commonly used genetic information resources. The diseases associated with each gene were evaluated via a semi-structured review of six genetic resources and an additional literature review using a natural language processing (NLP)-based procedure. Out of 27 candidate genes, 13 were identified as gastric cancer susceptibility genes (APC, ATM, BMPR1A, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH-Biallelic, PALB2, SMAD4, and STK11). A total of 145 gene–disease associations (with 45 unique diseases) were found to be associated with these 13 genes. Other gastrointestinal cancers were prominent among identified associations, with 11 of 13 gastric cancer susceptibility genes also associated with colorectal cancer, eight genes associated with pancreatic cancer, and seven genes associated with small intestine cancer. Gastric cancer susceptibility genes are frequently associated with other diseases as well as gastric cancer, with potential implications for how carriers of these genes are screened and managed. Unfortunately, commonly used genetic resources provide heterogeneous information with regard to these genes and their associated diseases, highlighting the importance of developing guides for clinicians that integrate data across available resources and the medical literature.

© 2018 American Academy of Hospice and Palliative Medicine Context: Clinicians document cancer patients’ symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped. Objectives: To create machine learning algorithms capable of extracting patient-reported symptoms from free-text electronic health record notes. Methods: The data set included 103,564 sentences obtained from the electronic clinical notes of 2695 breast cancer patients receiving paclitaxel-containing chemotherapy at two academic cancer centers between May 1996 and May 2015. We manually annotated 10,000 sentences and trained a conditional random field model to predict words indicating an active symptom (positive label), absence of a symptom (negative label), or no symptom at all (neutral label). Sentences labeled by human coder were divided into training, validation, and test data sets. Final model performance was determined on 20% test data unused in model development or tuning. Results: The final model achieved precision of 0.82, 0.86, and 0.99 and recall of 0.56, 0.69, and 1.00 for positive, negative, and neutral symptom labels, respectively. The most common positive symptoms were pain, fatigue, and nausea. Machine-based labeling of 103,564 sentences took two minutes. Conclusion: We demonstrate the potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy. Although our initial model requires further optimization to improve the performance, further model building may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications.