scholarly journals Quantitative and Qualitative Evaluation of a Deep Learning Auto Contouring Model for Prostate Cancer Patients With Hydrogel Spacer

2020 ◽  
Vol 108 (3) ◽  
pp. e297-e298
Author(s):  
S. Zieminski ◽  
J.A. Efstathiou ◽  
A.L. Zietman ◽  
S.C. Kamran ◽  
Y. Wang
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Qualitative Evaluation

scholarly journals Deep learning‐based synthetic CT generation for MR‐only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator

2021 ◽  
Author(s):  
Reza Farjam ◽  
Himanshu Nagar ◽  
Xi Kathy Zhou ◽  
David Ouellette ◽  
Silvia Chiara Formenti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Linear Accelerator ◽  
Synthetic Ct

scholarly journals Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 518
Author(s):  
Da-Chuan Cheng ◽  
Te-Chun Hsieh ◽  
Kuo-Yang Yen ◽  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Deep Learning ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Breast Cancer Patients ◽  
Location Detection ◽  
The Mean ◽  
Mean Sensitivity

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

scholarly journals Automatic detection and tracking of marker seeds implanted in prostate cancer patients using a deep learning algorithm

2021 ◽  
Vol 46 (2) ◽  
pp. 80
Author(s):  
Prabhakar Ramachandran ◽  
Keya Amarsee ◽  
Andrew Fielding ◽  
Margot Lehman ◽  
Christopher Noble ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Deep Learning Algorithm ◽  
Detection And Tracking

Expanding the diagnostic yield of germline genetic testing in cancer patients using deep learning.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1518-1518
Author(s):  
Saud H Aldubayan ◽  
Jake Conway ◽  
Leora Witkowski ◽  
Eric Kofman ◽  
Brendan Reardon ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Genetic Analysis ◽  
Cancer Patients ◽  
Standard Method ◽  
Gold Standard ◽  
Diagnostic Yield ◽  
Cancer Predisposition ◽  
Learning Approaches ◽  
Variant Detection

1518 Background: Germline genetic analysis is an essential tool for implementing precision cancer prevention and treatment. However, only a small fraction of cancer patients, even those with features suggestive of a cancer-predisposition syndrome, have detectable pathogenic germline events, which may in part reflect incomplete pathogenic variant detection by current gold-standard methods. Here, we leveraged deep learning approaches to expand the diagnostic utility of genetic analysis in cancer patients. Methods: Systematic analysis of the detection rate of pathogenic cancer-predisposition variants using the standard clinical variant detection method and a deep learning approach in germline whole-exome sequencing data of 2367 cancer patients (n = 1072 prostate cancer, 1295 melanoma). Results: Of 1072 prostate cancer patients, deep learning variant detection identified 16 additional prostate cancer patients with clinically actionable pathogenic cancer-predisposition variants that went undetected by the gold-standard method (198 vs. 182), yielding higher sensitivity (94.7% vs. 87.1%), specificity (64.0% vs. 36.0%), positive predictive value (95.7% vs. 91.9%), and negative predictive value (59.3% vs. 25.0%). Similarly, germline genetic analysis of 1295 melanoma patients showed that, compared with the standard method, deep learning detected 19 additional patients with validated pathogenic variants (93 vs. 74) with fewer false-positive calls (78 vs. 135) leading to a higher diagnostic yield. Collectively, deep learning identified one additional patient with a pathogenic cancer-risk variant, that went undetected by the standard method, for every 52 to 67 cancer patients undergoing germline analysis. Superior performance of deep learning, for detecting putative loss-of-function variants, was also seen across 5197 clinically relevant Mendelian genes in these cohorts. Conclusions: The gold-standard germline variant detection method, universally used in clinical and research settings, has significant limitations for identifying clinically relevant pathogenic disease-causing variants. We determined that deep learning approaches have a clinically significant increase in the diagnostic yield across commonly examined Mendelian gene sets.

Abstract 186: Gleason grade group predictions from mp-MRI of prostate cancer patients using auto deep learning

2021 ◽  
Author(s):  
Weiwei Zong ◽  
Eric Carver ◽  
Aharon Feldman ◽  
Joon Lee ◽  
Zhen Sun ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Gleason Grade ◽  
Grade Group

390: Durable Cancer Control in High Risk Prostate Cancer Patients Treated with Radical Prostatectomy

2007 ◽  
Vol 177 (4S) ◽  
pp. 130-130
Author(s):  
Markus Graefen ◽  
Jochen Walz ◽  
Andrea Gallina ◽  
Felix K.-H. Chun ◽  
Alwyn M. Reuther ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Radical Prostatectomy ◽  
Cancer Patients ◽  
Cancer Control ◽  
High Risk Prostate Cancer ◽  
Risk Prostate Cancer

598: Increased Risk of Newly Diagnosed Comorbidities in Prostate Cancer Patients Treated with Androgen Deprivation Therapy

2007 ◽  
Vol 177 (4S) ◽  
pp. 200-200 ◽  
Author(s):  
Andrea Gallina ◽  
Pierre I. Karakiewicz ◽  
Jochen Walz ◽  
Claudio Jeldres ◽  
Quoc-Dien Trinh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Androgen Deprivation Therapy ◽  
Androgen Deprivation ◽  
Newly Diagnosed ◽  
Increased Risk

289: Assessment of Indirect Costs of Newly Diagnosed Prostate Cancer Patients

2007 ◽  
Vol 177 (4S) ◽  
pp. 97-97
Author(s):  
Ravishankar Jayavedappa ◽  
Sumedha Chhatre ◽  
Richard Whittington ◽  
Alan J. Wein ◽  
S. Bruce Malkowicz
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
Indirect Costs ◽  
Newly Diagnosed
Sign in / Sign up

Export Citation Format

Share Document