Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone

Lymphatic spread determines treatment decisions in prostate cancer (PCa) patients. 68Ga-PSMA-PET/CT can be performed, although cost remains high and availability is limited. Therefore, computed tomography (CT) continues to be the most used modality for PCa staging. We assessed if convolutional neural networks (CNNs) can be trained to determine 68Ga-PSMA-PET/CT-lymph node status from CT alone. In 549 patients with 68Ga-PSMA PET/CT imaging, 2616 lymph nodes were segmented. Using PET as a reference standard, three CNNs were trained. Training sets balanced for infiltration status, lymph node location and additionally, masked images, were used for training. CNNs were evaluated using a separate test set and performance was compared to radiologists' assessments and random forest classifiers. Heatmaps maps were used to identify the performance determining image regions. The CNNs performed with an Area-Under-the-Curve of 0.95 (status balanced) and 0.86 (location balanced, masked), compared to an AUC of 0.81 of experienced radiologists. Interestingly, CNNs used anatomical surroundings to increase their performance, "learning" the infiltration probabilities of anatomical locations. In conclusion, CNNs have the potential to build a well performing CT-based biomarker for lymph node metastases in PCa, with different types of class balancing strongly affecting CNN performance

Peri-prostatic fat volume measurement as a predictive tool for castration resistance in advanced prostate cancer

Background: Obesity and aggressive prostate cancer (PC) may be linked, but how local peri-prostatic fat relates to tumour response following androgen deprivation therapy (ADT) is unknown. Objective: To test if peri-prostatic fat volume (PPFV) predicts tumour response to ADT. Design, setting, and participants: We performed a retrospective study on consecutive patients receiving primary ADT. From staging pelvic magnetic resonance imaging scans, the PPFV was quantified with OsirixX 6.5 imaging software. Statistical (univariate and multivariate) analysis were performed using R Version 3.2.1. Results and limitations: Of 224 consecutive patients, 61 with advanced (≥T3 or N1 or M1) disease had (3-mm high resolution axial sections) pelvic magnetic resonance imaging scan before ADT. Median age = 75 yr; median PPFV = 24.8 cm3 (range, 7.4–139.4 cm3). PPFV was significantly higher in patients who developed castration resistant prostate cancer (CRPC; n = 31), with a median of 37.9 cm3 compared with 16.1 cm3 (p < 0.0001, Wilcoxon rank sum test) in patients who showed sustained response to ADT (n = 30). Multivariate analysis using Cox proportional hazards models were performed controlling for known predictors of CRPC. PPFV was shown to be independent of all included factors, and the most significant predictor of time to CRPC. Using our multivariate model consisting of all known factors prior to ADT, PPFV significantly improved the area under the curve of the multivariate models receiver operating characteristic analysis. The main study limitation is a relatively small cohort to account for multiple variables, necessitating a future large-scale prospective analysis of PPFV in advanced PC. Conclusions: PPFV quantification in patients with advanced PC predicts tumour response to ADT

Predicting Pancreatic Cancer Using Support Vector Machine

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately

Pelvic lymph node dissection in prostate cancer staging : evaluation of morbidity and oncological outcomes

Background: To evaluate the morbidity of different surgical approaches for pelvic lymph node dissection (PLND), to evaluate the influence of morbidity on radiotherapy (RT) planning and to evaluate a possible therapeutic effect of a more extensive yield of PLND. Methods: From 2000-2016, 228 patients received staging PLND before primary RT in a single tertiary care center. Nine patients were excluded for the evaluation of morbidity. Fifty patients were operated in an open approach, 96 laparoscopic and 73 robot-assisted (RA). Clavien-Dindo classification was used for evaluating complications. Predictors of biochemical recurrence (BCR), clinical relapse (CR), cancer-specific survival (CSS) and overall survival (OS) were evaluated by regression analyses to determine a possible therapeutic effect. Results: Minimal invasive surgery (laparoscopic or RA) caused five times less major complications (22% vs. 4.3%, p = .001) and a median 3 days shorter hospital stay (5 days versus 2 days, p < .001). Major complications resulted in a delayed (23 days, p < .001) RT start but no oncological effect was seen. Independent oncological predictors were the number of positive nodes (BCR, CR, CSS, OS), a lower age (CR), a higher level of initial prostate-specific antigen (PSA) (BCR) and post-RT PSA (BCR). Conclusion: Minimal invasive surgery can diminish major complications which delay RT start. Nodal staging proved to be of importance for prognosis but no therapeutic effect was seen of performing PLND as such