DLS may aid diagnosing cancer on breast ultrasound images with an accuracy comparable to radiologists, and learns better and faster than a human reader with no prior experience. Further clinical trials with dedicated algorithms are warranted. Advances in knowledge: DLS can be trained classify cancer on breast ultrasound images high accuracy even with comparably few training cases. The fast evaluation speed makes real-time image analysis feasible.

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Current state-of-the-art artificial neural networks for general image analysis are able to detect cancer in mammographies with similar accuracy to radiologists, even in a screening-like cohort with low breast cancer prevalence.

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PurposeTo evaluate the accuracy of a deep learning software (DLS) in the discrimination between phyllodes tumors (PT) and fibroadenomas (FA).MethodsIn this IRB-approved, retrospective, single-center study, we collected all ultrasound images of histologically secured PT (n = 11, 36 images) and a random control group with FA (n = 15, 50 images). The images were analyzed with a DLS designed for industrial grade image analysis, with 33 images withheld from training for validation purposes. The lesions were also interpreted by four radiologists. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, negative and positive predictive values were calculated at the optimal cut-off (Youden Index).ResultsThe DLS was able to differentiate between PT and FA with good diagnostic accuracy (AUC = 0.73) and high negative predictive value (NPV = 100%). Radiologists showed comparable accuracy (AUC 0.60–0.77) at lower NPV (64–80%). When performing the readout together with the DLS recommendation, the radiologist’s accuracy showed a non-significant tendency to improve (AUC 0.75–0.87, p = 0.07).ConclusionDeep learning based image analysis may be able to exclude PT with a high negative predictive value. Integration into the clinical workflow may enable radiologists to more confidently exclude PT, thereby reducing the number of unnecessary biopsies.

Learning Objectives: On successful completion of this activity, participants should be able to describe (1) the various distinct clinical scenarios for prostate cancer; (2) the various imaging modalities available for use in prostate cancer; (3) the role of these imaging modalities in primary or recurrent prostate cancer and their advantages and disadvantages; and (4) the future potential of theranostic approaches and the evolving indications for contemporary hybrid imaging modalities such as PET/MRI and whole-body MRI. Financial Disclosure: Dr. Burger has received research grants and speaker honorarium from GE Healthcare. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest. CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through October 2022. Prostate cancer is a very heterogeneous disease, and contemporary management is focused on identification and treatment of the prognostically adverse high-risk tumors while minimizing overtreatment of indolent, low-risk tumors. In recent years, imaging has gained increasing importance in the detection, staging, posttreatment assessment, and detection of recurrence of prostate cancer. Several imaging modalities including conventional and functional methods are used in different clinical scenarios with their very own advantages and limitations. This continuing medical education article provides an overview of available imaging modalities currently in use for prostate cancer followed by a more specific section on the value of these different imaging modalities in distinct clinical scenarios, ranging from initial diagnosis to advanced, metastatic castration-resistant prostate cancer. In addition to established imaging indications, we will highlight some potential future applications of contemporary imaging modalities in prostate cancer.

BackgroundTexture analysis in oncological magnetic resonance imaging (MRI) may yield surrogate markers for tumor differentiation and staging, both of which are important factors in the treatment planning for cervical cancer.PurposeTo identify texture features which may predict tumor differentiation and nodal status in diffusion-weighted imaging (DWI) of cervical carcinomaMaterial and MethodsTwenty-three patients were enrolled in this prospective, institutional review board (IRB)-approved study. Pelvic MRI was performed at 3-T including a DWI echo-planar sequence with b-values 40, 300, and 800 s/mm2. Apparent diffusion coefficient (ADC) maps were used for region of interest (ROI)-based texture analysis (32 texture features) of tumor, muscle, and fat based on histogram and gray-level matrices (GLM). All features confounded by the ROI size (linear model) were excluded. The remaining features were examined for correlations with histological differentiation (Spearman) and nodal status (Kruskal–Wallis). Hierarchical cluster analysis was used to identify correlations between features. A P value < 0.05 was considered statistically significant.ResultsMean age was 55 years (range = 37–78 years). Biopsy revealed two well-differentiated, eight moderately differentiated, two moderately to poorly differentiated tumors, and five poorly differentiated tumors. Six tumors could not be graded. Lymph nodes were involved in 11 patients. Three GLM features correlated with the differentiation: LRHGE (ϱ = 0.53, P = 0.03), ZP (ϱ = –0.49, P < 0.05), and SZE (ϱ = –0.51, P = 0.04). Two histogram features, skewness (0.65 vs. 1.08, P = 0.04) and kurtosis (0.53 vs. 1.67, P = 0.02), were higher in patients with positive nodal status. Cluster analysis revealed several co-correlations.ConclusionWe identified potentially predictive GLM features for histological tumor differentiation and histogram features for nodal cancer stage.