CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis

Bhattacharya, Indrani; Seetharaman, Arun; Shao, Wei; Sood, Rewa; Kunder, Christian A.; Fan, Richard E.; Soerensen, Simon John Christoph; Wang, Jeffrey B.; Ghanouni, Pejman; Teslovich, Nikola C.; Brooks, James D.; Sonn, Geoffrey A.; Rusu, Mirabela

doi:10.1007/978-3-030-59713-9_31

Cited by 24 publications

(32 citation statements)

References 16 publications

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“…alignment approach 35 using average histograms derived from the training set of each MRI sequence independently. Standardized MRI intensities were then z-score normalized, similar to our prior studies 16,17 .…”

Section: Discussionmentioning

confidence: 99%

“…In order to standardize radiologist interpretations of prostate MRI, several machine learning methods have been developed to detect cancer, localize cancer, and characterize cancer aggressiveness using prostate MR images. Prior machine learning methods for prostate cancer detection include traditional machine learning 9,10,11,12 as well as deep learning models using MRI 13,14,15,16,17,18 . The prior studies for automated prostate cancer detection and localization on MRI not only differ in the models used, but also in the ground truth labels used to train their models (Table 1).…”

Section: Introductionmentioning

confidence: 99%

“…INTRODUCTION 5. Pathologist outlines on whole-mount histopathology images mapped onto pre-operative MRI using automated MRI-histopathology registration 16 ; 6. Gleason pattern labels on whole-mount histopathology images derived from a previously validated deep learning algorithm 21 mapped onto MRI through automated MRI-histopathology registration 17,22 ;…”

Section: Introductionmentioning

confidence: 99%

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Bridging the gap between prostate radiology and pathology through machine learning

Bhattacharya,

Lim,

Aung

et al. 2021

Preprint

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Background: Prostate cancer remains the second deadliest cancer for American men despite clinical advancements. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements.Purpose: Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning 1 Currently at Facebook 2 Currently at Momenta.ai

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bridging the gap between prostate radiology and pathology through machine learning

Bhattacharya,

Lim,

Aung

et al. 2021

Preprint

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“… 24 We previously used a subset of the unique dataset generated by RAPSODI 22 to train a deep learning model to automatically detect prostate cancer on MRI. 11 Here, we seek to expand upon this work by focusing on distinguishing aggressive from indolent cancers on MRI using labels derived from automated registration of histopathology and MR images. Unlike prior methods that either use radiologist labels or pathology labels mapped from cognitive alignment (radiologists and pathologists jointly reviewing the MR and histopathology images, without computational alignment), our proposed approach is the first to use automatically detected aggressive and indolent cancers on histopathology images 28 mapped onto MRI to generate labels for aggressive and indolent cancers on MRI.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging

et al. 2021

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Purpose While multi‐parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per‐pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. Methods We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole‐mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per‐pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists. Results Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer. Conclusions Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.

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“…Such mapping allows side-by-side comparison of the histopathology and MRI images, which can be use in the training of radiologists to improve their interpretation of MRI. Furthermore, accurate cancer labels achieved by image registration may facilitate the development of radiomic and deep learning approaches for early prostate cancer detection and risk stratification on pre-operative MRI Cao et al (2019) ; Lovegrove et al (2016) ; Wang et al (2018) ; ( Bhattacharya et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate

Shao

Banh

Kunder

et al. 2021

Medical Image Analysis

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Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet .

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CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis

Cited by 24 publications

References 16 publications

Bridging the gap between prostate radiology and pathology through machine learning

Bridging the gap between prostate radiology and pathology through machine learning

Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate

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