Convolutional neural networks for prostate cancer recurrence prediction

Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

doi:10.1117/12.2255774

Cited by 32 publications

(27 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kumar et alius [ 23 ] also consider tissue images to detect prostate cancer through deep learning. They obtain an accuracy equal to 0.81 considering three convolutional layers, while in the deep neural network we designed we consider four convolutional layers (with the aim to extract more complex features) obtaining an accuracy ranging from 0.96 to 0.98 using radiomic features.…”

Section: Discussionmentioning

confidence: 99%

Radiomics for Gleason Score Detection through Deep Learning

Brunese

Mercaldo

Reginelli

et al. 2020

Sensors

View full text Add to dashboard Cite

Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising: they are ranging between 0.96 and 0.98 for Gleason score prediction.

show abstract

Section: Discussionmentioning

confidence: 99%

Radiomics for Gleason Score Detection through Deep Learning

Brunese

Mercaldo

Reginelli

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…10,11 Notably, CAMELYON16, a nodal metastasis detection contest, resulted in a GoogLeNet algorithm outperforming pathologists in time-limited classification of hematoxylin-eosin lymph node metastasis. 11,12 Several studies have also investigated DL in the context of prostatic, 10,[13][14][15][16][17][18][19] neurologic, 20-22 and colonic [23][24][25] neoplasms.…”

mentioning

confidence: 99%

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Martín

Hanson

Gullapalli

et al. 2019

Archives of Pathology &Amp; Laboratory Medicine

View full text Add to dashboard Cite

Context.-Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.Objective.-To investigate the use of DL for nonneoplastic gastric biopsies.Design.-Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion.Results.-For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] ¼ 99.7%) and H pylori (AUC ¼ 100%), and 40% for reactive gastropathy (AUC ¼ 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/ specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%).Conclusions.-A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

show abstract

“…Furthermore, possible prognostic markers could be extracted from histopathology images analyzed using image-based machine learning. Previous studies have proven that image-based machine learning can predict recurrence of prostate cancer [26]. Prediction of patient outcome has been performed in gliomas [27], pan-renal cell carcinoma [28] and colorectal cancer [29,30] with the use of machine learning models based on histopathology images.…”

Section: Discussionmentioning

confidence: 99%

Recurrence in Non-Muscle Invasive Bladder Cancer Patients: External Validation of the EORTC, CUETO and EAU Risk Tables and Towards a Non-Linear Survival Model

Lucas

Jansen

Oddens

et al. 2020

BLC

View full text Add to dashboard Cite

BACKGROUND: EORTC, CUETO and EAU are the most commonly used risk stratification models for recurrence and progression in non-muscle invasive bladder cancer (NMIBC). OBJECTIVE: We assessed the predictive value of the EORTC, CUETO and EAU risk group stratification methods for our population and explore options to improve the predictive value using Cox Proportional Hazards (CPH), Boosted Cox regression and a non-linear Random Survival Forest (RSF) model. MATERIALS: Our retrospective database included of 452 NMIBC patients who underwent a transurethral resection of bladder tumor (TURBT) between 2000 and 2018 in our hospital. The cumulative incidence of recurrence was calculated at one- and five-years for all risk stratification methods. A customized CPH, Boosted Cox and RSF models were trained in order to predict recurrence, and the performances were compared. RESULTS: Risk stratification using the EORTC, CUETO and EAU showed small differences in recurrence probabilities between the risk groups as determined by the risk stratification. The concordance indices (C-index) were low and ranged between 0.51 and 0.57. The predictive accuracies of CPH, Boosted Cox and RSF models were also moderate, with C-indices ranging from 0.61 to 0.64. CONCLUSIONS: Prediction of recurrence in patients with NMIBC based on patient characteristics is difficult. Alternative (non-linear) approaches have the potential to improve the predictive value. Nonetheless, the currently used characteristics are unable to properly stratify between the recurrence risks of patients.

show abstract

Convolutional neural networks for prostate cancer recurrence prediction

Cited by 32 publications

References 24 publications

Radiomics for Gleason Score Detection through Deep Learning

Radiomics for Gleason Score Detection through Deep Learning

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology

Recurrence in Non-Muscle Invasive Bladder Cancer Patients: External Validation of the EORTC, CUETO and EAU Risk Tables and Towards a Non-Linear Survival Model

Contact Info

Product

Resources

About