R ecent highest-level evidence is mounting that there is an unequivocal benefit of MRI-targeted biopsies replacing or complementing systematic biopsies for diagnosis of prostate cancer (1-3). Realizing the integral role of MRI in diagnosis of prostate cancer, the Prostate Imaging Reporting and Data System (PI-RADS) continues to be developed (4-7). PI-RADS allows standardization of prostate MRI interpretation, which is a difficult task due to heterogeneous signal changes from benign prostatic hyperplasia, inflammation, and scarring after biopsy mimicking or hiding the appearance of prostate cancer. Even with PI-RADS, the high level of expertise required for accurate interpretation and persistent interobserver variability (8) limit consistency and availability while the demand for prostate MRI interpretation is at unprecedented levels. As convolutional neural networks approach or exceed human performance in natural image analysis (9), they promise to revolutionize computer-aided diagnosis and are increasingly evaluated in prostate MRI (10-15). U-Net is a convolutional neural network architecture optimized for image segmentation. It consists of an encoder and decoder

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Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.8) with an indication for MRI-transrectal US fusion biopsy between May 2015 and September 2016 (training cohort, 183 patients; test cohort, 133 patients). Lesions identified by prospective clinical readings were manually segmented for mean ADC and radiomics analysis. Global and zone-specific random forest RML and mean ADC models for classification of clinically significant prostate cancer (Gleason grade group ≥ 2) were developed on the training set and the fixed models tested on an independent test set. Clinical readings, mean ADC, and radiomics were compared by using the McNemar test and receiver operating characteristic (ROC) analysis. Results In the test set, radiologist interpretation had a per-lesion sensitivity of 88% (53 of 60) and specificity of 50% (79 of 159). Quantitative measurement of the mean ADC (cut-off 732 mm/sec) significantly reduced false-positive (FP) lesions from 80 to 60 (specificity 62% [99 of 159]) and false-negative (FN) lesions from seven to six (sensitivity 90% [54 of 60]) (P = .048). Radiologist interpretation had a per-patient sensitivity of 89% (40 of 45) and specificity of 43% (38 of 88). Quantitative measurement of the mean ADC reduced the number of patients with FP lesions from 50 to 43 (specificity 51% [45 of 88]) and the number of patients with FN lesions from five to three (sensitivity 93% [42 of 45]) (P = .496). Comparison of the area under the ROC curve (AUC) for the mean ADC (AUC = 0.84; AUC ≤ 0.87) vs the RML (AUC = 0.88, P = .176; AUC ≤ 0.89, P ≥ .493) showed no significantly different performance. Conclusion Quantitative measurement of the mean apparent diffusion coefficient (ADC) improved differentiation of benign versus malignant prostate lesions, compared with clinical assessment. Radiomic machine learning had comparable but not better performance than mean ADC assessment. © RSNA, 2018 Online supplemental material is available for this article.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

To determine the sensitivity and specificity of HPV16 serology as diagnostic marker for HPV16-driven oropharyngeal squamous cell carcinoma (OPSCC), 214 HNSCC patients from Germany and Italy with fresh-frozen tumor tissues and sera collected before treatment were included in this study. Hundred and twenty cancer cases were from the oropharynx and 94 were from head and neck cancer regions outside the oropharynx (45 oral cavity, 12 hypopharynx and 35 larynx). Serum antibodies to early (E1, E2, E6 and E7) and late (L1) HPV16 proteins were analyzed by multiplex serology and were compared to tumor HPV RNA status as the gold standard. A tumor was defined as HPV-driven in the presence of HPV16 DNA and HPV16 transformation-specific RNA transcript patterns (E6*I, E1 E4 and E1C). Of 120 OPSCC, 66 (55%) were HPV16-driven. HPV16 E6 seropositivity was the best predictor of HPV16-driven OPSCC (diagnostic accuracy 97% [95%CI 92-99%], Cohen's kappa 0.93 [95%CI 0.8-1.0]). Of the 66 HPV-driven OPSCC, 63 were HPV16 E6 seropositive, compared to only one (1.8%) among the 54 non-HPV-driven OPSCC, resulting in a sensitivity of 96% (95%CI 88-98) and a specificity of 98% (95%CI 90-100). Of 94 HNSCC outside the oropharynx, six (6%) were HPV16-driven. In these patients, HPV16 E6 seropositivity had lower sensitivity (50%, 95%CI 19-81), but was highly specific (100%, 95%CI 96-100). In conclusion, HPV16 E6 seropositivity appears to be a highly reliable diagnostic marker for HPV16-driven OPSCC with very high sensitivity and specificity, but might be less sensitive for HPV16-driven HNSCC outside the oropharynx.

The broad clinical spectrum of neuroblastoma ranges from spontaneous regression to rapid progression despite intensive multimodal therapy. This diversity is not fully explained by known genetic aberrations, suggesting the possibility of epigenetic involvement in pathogenesis. In pursuit of this hypothesis, we took an integrative approach to analyze the methylomes, transcriptomes, and copy number variations in 105 cases of neuroblastoma, complemented by primary tumor- and cell line-derived global histone modification analyses and epigenetic drug treatment in vitro We found that DNA methylation patterns identify divergent patient subgroups with respect to survival and clinicobiologic variables, including amplified MYCN Transcriptome integration and histone modification-based definition of enhancer elements revealed intragenic enhancer methylation as a mechanism for high-risk-associated transcriptional deregulation. Furthermore, in high-risk neuroblastomas, we obtained evidence for cooperation between PRC2 activity and DNA methylation in blocking tumor-suppressive differentiation programs. Notably, these programs could be re-activated by combination treatments, which targeted both PRC2 and DNA methylation. Overall, our results illuminate how epigenetic deregulation contributes to neuroblastoma pathogenesis, with novel implications for its diagnosis and therapy. Cancer Res; 76(18); 5523-37. ©2016 AACR.

1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.

As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.