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

scholarly journals Robustness and generalizability of deep learning synthetic CT for PET/MRI-based radiotherapy planning of head and neck cancer patients

2021 ◽  
pp. 100762
Author(s):  
Anders B Olin ◽  
Christopher Thomas ◽  
Adam E Hansen ◽  
Jacob H Rasmussen ◽  
Georgios Krokos ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Radiotherapy Planning ◽  
Synthetic Ct

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.

scholarly journals MV CBCT-Based Synthetic CT Generation Using a Deep Learning Method for Rectal Cancer Adaptive Radiotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Jun Zhao ◽  
Zhi Chen ◽  
Jiazhou Wang ◽  
Fan Xia ◽  
Jiayuan Peng ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Deep Learning ◽  
Image Quality ◽  
Cancer Patients ◽  
Model Performance ◽  
Hounsfield Unit ◽  
Adaptive Radiotherapy ◽  
Dice Similarity Coefficient ◽  
Learning Method ◽  
Synthetic Ct

Due to image quality limitations, online Megavoltage cone beam CT (MV CBCT), which represents real online patient anatomy, cannot be used to perform adaptive radiotherapy (ART). In this study, we used a deep learning method, the cycle-consistent adversarial network (CycleGAN), to improve the MV CBCT image quality and Hounsfield-unit (HU) accuracy for rectal cancer patients to make the generated synthetic CT (sCT) eligible for ART. Forty rectal cancer patients treated with the intensity modulated radiotherapy (IMRT) were involved in this study. The CT and MV CBCT images of 30 patients were used for model training, and the images of the remaining 10 patients were used for evaluation. Image quality, autosegmentation capability and dose calculation capability using the autoplanning technique of the generated sCT were evaluated. The mean absolute error (MAE) was reduced from 135.84 ± 41.59 HU for the CT and CBCT comparison to 52.99 ± 12.09 HU for the CT and sCT comparison. The structural similarity (SSIM) index for the CT and sCT comparison was 0.81 ± 0.03, which is a great improvement over the 0.44 ± 0.07 for the CT and CBCT comparison. The autosegmentation model performance on sCT for femoral heads was accurate and required almost no manual modification. For the CTV and bladder, although modification was needed for autocontouring, the Dice similarity coefficient (DSC) indices were high, at 0.93 and 0.94 for the CTV and bladder, respectively. For dose evaluation, the sCT-based plan has a much smaller dose deviation from the CT-based plan than that of the CBCT-based plan. The proposed method solved a key problem for rectal cancer ART realization based on MV CBCT. The generated sCT enables ART based on the actual patient anatomy at the treatment position.

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
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