Transparent Machine Learning Models to Diagnose Suspicious Thoracic Lesions Leveraging CT Guided Biopsy Data.

RATIONALE AND OBJECTIVES: To train and validate machine learning models capable of classifying suspicious thoracic lesions as benign or malignant and to further classify malignant lesions by pathologic subtype while quantifying feature importance for each classification. MATERIALS AND METHODS: 796 patients who had undergone CT guided thoracic biopsy for a concerning thoracic lesion (79.3% lung, 11.4% mediastinum, 6.5% pleura, 2.7% chest wall) were retrospectively enrolled. Lesions were classified as malignant or benign based on ground-truth pathology result, and malignant lesions were classified as primary or secondary cancer. Clinical variables were extracted from EMR and radiology reports. Supervised binary and multiclass classification models were trained to classify lesions based on the input features and evaluated on a held-out test set. Model specific feature analyses were performed to identify variables most predictive of each class, as well as to assess the independent importance of clinical, and imaging features. RESULTS: Binary classification models achieved a top accuracy of 80.6%, with predictive features included smoking history, age, lesion size, and lesion location. Multiclass classification models achieved a top weighted average f1-score of 0.73. Features predictive of primary cancer included smoking history, race, and age, while features predictive of secondary cancer included lesion location, and a history of cancer. CONCLUSION: Machine learning models enable classification of suspicious thoracic lesions based on clinical and imaging variables, achieving clinically useful performance while identifying importance of individual input features on a pathology-proven dataset. We believe models such as these are more likely to be trusted and adopted by clinicians.

Gender-based Disparities in Receipt of Care and Survival in Malignant Pleural Mesothelioma.

BACKGROUND: Despite accounting for a minority of malignant pleural mesothelioma (MPM) diagnoses, females may experience differential survival relative to males. It is unclear if there are gender-based differences in receipt of treatment or disease-related outcomes for patients with MPM. We therefore utilized the National Cancer Database (NCDB) to assess patterns-of-care and overall survival (OS) among patients with MPM by gender. MATERIALS AND METHODS: Patients with histologically confirmed MPM treated from 2004 to 2013 were identified from the NCDB. The association between female gender and OS was assessed using multivariable Cox proportional hazards models with propensity score matching. Patterns-of-care were assessed using multivariable logistic regression. The overall treatment effect was tested in subsets of patients by treatment strategy, histology, and clinical stage. RESULTS: A total of 18,799 patients were identified, of whom 14,728 (78%) were male and 4071 (22%) were female. Females were statistically more likely to present at a younger age, with fewer comorbidities, and with epithelioid histology. Despite these favorable prognostic features, women were less likely to receive surgery (P ≤ .001) or chemotherapy (P ≤ .001) compared with males. On multivariable analysis, female gender was associated with improved OS (hazard ratio, 0.83; 95% confidence interval, 0.80-0.86; P ≤ .001). Gender-based survival differences were seen across all stages, but only among patients with epithelioid (P ≤ .001) and not biphasic (P = .17) or sarcomatoid (P = 1.00) histology. CONCLUSIONS: Surgery and chemotherapy are disproportionately underutilized in female patients with MPM. Despite this concerning disparity, female gender is independently associated with improved survival relative to males. Further research to understand factors that lead to gender disparities in MPM is warranted.

Treatment of malignant pleural mesothelioma with chemotherapy preceding versus after surgical resection.

OBJECTIVES: There are 2 main treatment paradigms recognized by the National Comprehensive Cancer Network for resectable malignant pleural mesothelioma (MPM): induction chemotherapy followed by resection (IC/R), and up-front resection with postoperative chemotherapy (R/PC). These paradigms are being compared in an accruing randomized phase II trial. In the absence of such completed trials, in this study we evaluated overall survival (OS) and postoperative outcomes of IC/R and R/PC. METHODS: The National Cancer Database was queried for newly diagnosed epithelioid/biphasic MPM. Metastatic, node-positive, and/or cT4 disease was excluded, along with nondefinitive surgery and lack of chemotherapy. Multivariable logistic regression ascertained factors independently associated with induction chemotherapy delivery. Kaplan-Meier analysis was used to evaluate OS between cohorts; multivariable Cox proportional hazards modeling was used to assess factors associated with OS. Survival was also evaluated between propensity-matched populations. Last, postoperative outcomes were assessed between groups. RESULTS: Overall, 361 patients (182 IC/R, 179 R/PC) were analyzed. Temporal trends revealed that IC/R is decreasing over time. Survival of the IC/R cohort was similar to that of R/PC patients (20.9 vs 21.7 months; P = .500); this persisted after propensity matching (20.8 vs 22.0 months; P = .270). However, patients who underwent IC/R experienced longer postoperative hospitalization (median 7 days vs 6 days; P = .001) and higher 30-day mortality (3.3% vs 0%; P = .020). CONCLUSIONS: To our knowledge, this is the only comparative investigation of the 2 major management paradigms of operable MPM. IC/R regimens are decreasing over time in the United States. Although associated with survival similar to R/PC, IC/R might be associated with worse postoperative outcomes. Careful induction chemotherapy patient selection is thus highly recommended.

Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.

PURPOSE: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single-model methods (e.g., logistic regression and decision trees) in predictive modeling of post-RT treatment failure and adverse events (AEs) for breast cancer patients using automatically extracted EMR data. METHODS: Data from 1967 consecutive breast radiotherapy (RT) courses at one institution between 2008 and 2015 were automatically extracted from EMRs and oncology information systems using extraction software. Over 230 variables were extracted spanning the following variable segments: patient demographics, medical/surgical history, tumor characteristics, RT treatment history, and AEs tracked using CTCAEv4.0. Treatment failure was extracted algorithmically by searching posttreatment encounters for evidence of local, nodal, or distant failure. Individual models were trained using decision trees, logistic regression, random forests, and boosted decision trees to predict treatment failures and AEs. Models were fit on 75% of the data and evaluated for probability calibration and area under the ROC curve (AUC) on the remaining test set. The impact of each variable segment was assessed by retraining without the segment and measuring change in AUC (ΔAUC). RESULTS: All AUC values were statistically significant (P < 0.05). Ensemble methods outperformed single-model methods across all outcomes. The best ensemble method outperformed decision trees and logistic regression by an average AUC of 0.053 and 0.034, respectively. Model probabilities were well calibrated as evidenced by calibration curves. Excluding the patient medical history variable segment led to the largest AUC reduction in all models (Average ΔAUC = -0.025), followed by RT treatment history (-0.021) and tumor information (-0.015). CONCLUSION: In this largest such study in breast cancer performed to date, automatically extracted EMR data provided a basis for reliable outcome predictions across multiple statistical methods. Ensemble methods provided substantial advantages over single-model methods. Patient medical history contributed the most to prediction quality.

Survival by Histologic Subtype of Malignant Pleural Mesothelioma and the Impact of Surgical Resection on Overall Survival.

INTRODUCTION: For the 3 histologic subtypes of malignant pleural mesothelioma (MPM)-epithelioid, sarcomatoid, and biphasic-the magnitude of benefit with surgical management remains underdefined. MATERIALS AND METHODS: The National Cancer Data Base was queried for newly diagnosed nonmetastatic MPM with known histology. Patients in each histologic group were dichotomized into those receiving gross macroscopic resection versus lack thereof/no surgery. Kaplan-Meier analysis evaluated overall survival (OS) between cohorts; multivariable Cox proportional hazards modeling assessed factors associated with OS. After propensity matching, survival was evaluated for each histologic subtype with and without surgery. RESULTS: Overall, 4207 patients (68% epithelioid, 18% sarcomatoid, 13% biphasic) met the study criteria. Before propensity matching, patients with epithelioid disease experienced the highest median OS (14.4 months), followed by biphasic (9.5 months) and sarcomatoid (5.3 months) disease; this also persisted after propensity matching (P < .001). After propensity matching, surgery was associated with significantly improved OS for epithelioid (20.9 vs. 14.7 months, P < .001) and biphasic (14.5 vs. 8.8 months, P = .013) but not sarcomatoid (11.2 vs. 6.5 months, P = .140) disease. On multivariable analysis, factors predictive of poorer OS included advanced age, male gender, uninsured status, urban residence, treatment at community centers, and T4/N2 disease (all P < .05). Chemotherapy and surgery were independently associated with improved OS, as was histology (all P < .001). CONCLUSION: This large investigation evaluated surgical practice patterns and survival by histology for MPM and found that histology independently affects survival. Gross macroscopic resection is associated with significantly increased survival in epithelioid and biphasic, but not sarcomatoid, disease. However, the decision to perform surgery should continue to be individualized in light of available randomized data.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

Facility volume and postoperative outcomes for malignant pleural mesothelioma: A National Cancer Data Base analysis.

PURPOSE: This study of a large, contemporary national database evaluated postoperative outcomes and overall survival (OS) for malignant pleural mesothelioma (MPM) by facility volume. METHODS: The National Cancer Database was queried for newly-diagnosed non-metastatic MPM undergoing definitive surgery (extrapleural pneumonectomy (EPP) or pleurectomy/decortication (P/D)). Patients were dichotomized into those receiving therapy at a high-volume facility (HVF), defined a priori at the 90th percentile of case volume, with all others categorized as lower-volume facilities (LVFs). Statistics included multivariable logistic regression, Kaplan-Meier analysis, propensity-matching, and multivariable Cox proportional hazards modeling. Sensitivity analysis varied the dichotomized HVF-LVF cutoff and evaluated effects on postoperative outcomes and OS. RESULTS: Of 1307 patients, 621 (48%) were treated at LVFs and 686 (52%) at HVFs. HVFs were more often in the Middle/South Atlantic regions, and less likely in New England, South, and Midwest. Notably, 75% of procedures at HVFs were P/Ds, versus 84% at LVFs (p < 0.001). Patients treated at HVFs experienced shorter length of postoperative hospitalization (p = 0.035), lower 30-day readmission rates (4.6% vs. 6.1%, p = 0.021), and lower 90-day mortality rates (10.0% vs. 14.6%, p = 0.029). Median OS for respective groups were 18 versus 15 months (p = 0.010), which were not significant following propensity-matching (p = 0.540). On multivariable analysis, facility volume did not independently predict for OS. Sensitivity analyses confirmed the postoperative outcomes and OS findings. CONCLUSIONS: This is the largest investigation to date assessing facility volume and outcomes following surgery for MPM. Although no independent effects on OS were observed, postoperative outcomes were more favorable at HVFs. These findings have implications for postoperative management, patient counseling, referring providers, and cost-effectiveness.

National Cancer Database Report on Pneumonectomy Versus Lung-Sparing Surgery for Malignant Pleural Mesothelioma.

INTRODUCTION: Controversy exists regarding the optimal surgical technique for malignant pleural mesothelioma (MPM). We evaluated national practice patterns and outcomes of MPM treated with extrapleural pneumonectomy (EPP) versus lung-sparing extended pleurectomy/decortication (P/D). METHODS: The National Cancer Database was queried for patients with newly diagnosed MPM undergoing EPP or P/D. Multivariable logistic regression ascertained clinical factors independently associated with P/D receipt. Kaplan-Meier analysis was used to evaluate overall survival (OS) between cohorts; multivariable Cox proportional hazards modeling was used to evaluate factors associated with OS. Survival was then evaluated between propensity-matched populations. RESULTS: Overall, 1307 patients (271 undergoing EPP [21%] and 1036 undergoing P/D [79%]) met the criteria. Patients receiving P/D were older (p = 0.028), whereas those undergoing EPP were more likely to live in a rural area (p = 0.044), live farther from the treating facility (p = 0.039), and receive treatment at an academic center (p = 0.050). There were no differences between cohorts in 30-day readmission or mortality (all p > 0.05). The median OS times in the EPP and P/D groups were 19 versus 16 months, respectively (p = 0.120); no differences were observed after propensity matching (p = 0.540). CONCLUSIONS: In this largest analysis of its kind to date, findings from this contemporary cohort demonstrate that P/D comprised most surgical procedures for MPM. Procedure type was influenced by sociodemographic and geographical factors, without observed differences in survival or postoperative mortality and readmission rates between techniques.