Citations

PURPOSE SARC024 is a phase II clinical trial of the multikinase inhibitor regorafenib in specific sarcoma subtypes, including advanced osteosarcoma. We hypothesized that regorafenib would improve progression-free survival (PFS) in patients with sarcoma and report the results of the osteosarcoma cohort. PATIENTS AND METHODS This trial enrolled patients with progressive metastatic osteosarcoma with measurable disease by RECIST who had received at least one prior line of therapy. Patients were randomly assigned at a ratio of one to one to regorafenib or placebo. Crossover was allowed at time of disease progression. PFS was the primary end point of the study, which was powered to detect a difference of at least 3 months in median PFS. RESULTS Forty-two patients from 12 centers were enrolled between September 2014 and May 2018. Median age was 37 years (range, 18 to 76 years). Patients had received an average of 2.3 prior therapy regimens. Ten patients receiving placebo crossed over to active drug at time of progression. Study enrollment was stopped early, after a data safety monitoring committee review. Median PFS was significantly improved with regorafenib versus placebo: 3.6 months (95% CI, 2.0 to 7.6 months) versus 1.7 months (95% CI, 1.2 to 1.8 months), respectively (hazard ratio, 0.42; 95% CI, 0.21 to 0.85; P = .017). In the context of the crossover design, there was no statistically significant difference in overall survival. Fourteen (64%) of 22 patients initially randomly assigned to regorafenib experienced grade 3 to 4 events attributed to treatment, including one grade 4 colonic perforation. CONCLUSION The study met its primary end point, demonstrating activity of regorafenib in patients with progressive metastatic osteosarcoma. No new safety signals were observed. Regorafenib should be considered a treatment option for patients with relapsed metastatic osteosarcoma.

AbstractObjectivesWe sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12‐lead electrocardiogram (ECG) in a large prospective cohort.BackgroundPatients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging.MethodsWe applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new “positive screens.”ResultsAmong 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 “false‐positives screens,” 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT‐pro‐BNP after the initial “positive screen.”ConclusionsA deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes.

PURPOSE Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and validate a deep learning model using image convolution to automatically categorize BPU on MBI. METHODS MBI examinations obtained for clinical and research purposes from 2004 to 2015 were reviewed to classify the BPU pattern using a standardized five-category scale. Two expert radiologists provided interpretations that were used as the reference standard for modeling. The modeling consisted of training and validating a convolutional neural network to predict BPU. Model performance was summarized in data reserved to test the performance of the algorithm at the per-image and per-breast levels. RESULTS Training was performed on 24,639 images from 3,133 unique patients. The model performance on the withheld testing data (6,172 images; 786 patients) was evaluated. Using direct matching on the predicted classification resulted in an accuracy of 69.4% (95% CI, 67.4% to 71.3%), and if prediction within one category was considered, accuracy increased to 96.0% (95% CI, 95.2% to 96.7%). When considering the breast-level prediction of BPU, the accuracy remained strong, with 70.3% (95% CI, 68.0% to 72.6%) and 96.2% (95% CI, 95.3% to 97.2%) for the direct match and allowance for one category, respectively. CONCLUSION BPU provided a robust target for training a convolutional neural network. A validated computer algorithm will allow for objective, reproducible encoding of BPU to foster its integration into risk-stratification algorithms.