Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy.

PURPOSE: Auto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer. METHODS AND MATERIALS: Three contouring workflows were defined based on the initial contour-generation method including manual (MAN), atlas-based auto-contour (ATLAS), and deep-learning auto-contour (DEEP). For each workflow, initial contour generation was retrospectively performed on 15 patients with prostate cancer. Then, radiation oncologists (ROs) edited each contour while blinded to the manner in which the initial contour was generated. Workflows were compared by time (both in initial contour generation and in RO editing), contour similarity, and dosimetric evaluation. RESULTS: Mean durations for initial contour generation were 10.9 min, 1.4 min, and 1.2 min for MAN, DEEP, and ATLAS, respectively. Initial DEEP contours were more geometrically similar to initial MAN contours. Mean durations of the RO editing steps for MAN, DEEP, and ATLAS contours were 4.1 min, 4.7 min, and 10.2 min, respectively. The geometric extent of RO edits was consistently larger for ATLAS contours compared with MAN and DEEP. No differences in clinically relevant dose-volume metrics were observed between workflows. CONCLUSION: Auto-contouring software affords time savings for initial contour generation; however, it is important to also quantify workload changes at the RO editing step. Using deep-learning auto-contouring for bladder and rectum contour generation reduced contouring time without negatively affecting RO editing times, contour geometry, or clinically relevant dose-volume metrics. This work contributes to growing evidence that deep-learning methods are a clinically viable solution for organ-at-risk contouring in radiation therapy.

Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.

PURPOSE: To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms. METHODS: Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected ccRCCs were retrospectively identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (gray-level co-occurrences (GLC) and gray-level run-lengths (GLRL)) features were calculated for each tumor in each phase. T testing compared feature values in low- and high-grade ccRCCs, with a (Benjamini-Hochberg) false discovery rate of 10%. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of low- and high-grade ccRCCs in each phase. Histogram, texture, and combined histogram and texture data sets were used to train and test four algorithms (k-nearest neighbor (KNN), support vector machine (SVM), random forests, and decision tree) with tenfold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of low- and high-grade ccRCCs. RESULTS: Zero, 23, and 0 features in the NC, CM, and NG phases had statistically significant differences between low and high-grade ccRCCs. CM histogram skewness and GLRL short run emphasis had the highest AUCs (0.82) in predicting histologic grade. All four algorithms had the highest AUCs (0.97) predicting histologic grade using CM histogram features. The algorithms' AUCs decreased using histogram or texture features from NC or NG phases. CONCLUSION: The histologic grade of small ccRCCs can be accurately predicted with machine learning algorithms using CM histogram features, which outperform NC and NG phase image data.

Prognostic factors that predict failure of manipulation under anesthesia for the stiff total knee arthroplasty: A systematic review.

PURPOSE: Prognostic factors associated with Manipulation under anesthesia (MUA) failure remain unknown. METHODS: A systematic review of the literature was performed to identify studies that reported prognostic factors associated with MUA for postoperative stiffness. RESULTS: 7 studies analyzing prognostic factors associated with MUA outcomes were included. Several studies note pre-MUA ROM to be a significant prognostic factor affecting post-MUA ROM at final follow-up. Knees with <70° of flexion pre-MUA had less final flexion arc than those with >70°. CONCLUSIONS: The strongest prognostic factor for decreased ROM after MUA is severe pre-MUA stiffness.

Efficacy of Manipulation Under Anesthesia for Stiffness Following Total Knee Arthroplasty: A Systematic Review.

BACKGROUND: Knee stiffness following primary total knee arthroplasty can lead to unsatisfactory patient outcomes secondary to persistent pain and loss of function. Manipulation under anesthesia (MUA) remains a viable option for treatment of post-operative stiffness. However, the optimal timing and clinical efficacy of manipulation of anesthesia remains unknown. METHODS: A systematic review of the literature was performed to identify studies that reported clinical outcomes for patients who underwent MUA for post-operative stiffness treatment. Repeat MUA procedures were included in the study but were analyzed separately. RESULTS: Twenty-two studies (1488 patients) reported on range of motion (ROM) after MUA, and 4 studies (81 patients) reported ROM after repeat MUA. All studies reported pre-MUA motion of less than 90°, while mean ROM at last follow-up exceeded 90° in all studies except 2. For studies reporting ROM improvement following repeat MUA, the mean pre-manipulation ROM was 80° and the mean post-manipulation ROM was 100.6°. CONCLUSION: MUA remains an efficacious, minimally invasive treatment option for post-operative stiffness following TKA. MUA provides clinically significant improvement in ROM for most patients, with the best outcomes occurring in patients treated within 12 weeks post-operatively. PROSPERO REGISTRATION NUMBER: CRD42016052215.