Automated Gleason grading of prostate cancer tissue microarrays via deep learning.
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ABSTRACT:
SUBMITTER: Arvaniti E
PROVIDER: S-EPMC6089889 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC8104238 | biostudies-literature
Project description:The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3-82.7%), the Cohen's kappa score (κ) was 0.650 (95% CI: 0.570-0.730), and the quadratic-weighted kappa score (κquad) was 0.897 (95% CI: 0.815-0.979). When trained on 621 cases from one institution and validated on 167 cases from the other institution, the system's accuracy reached 67.4% (95% CI: 63.2-71.6%), κ 0.553 (95% CI: 0.495-0.610), and the κquad 0.880 (95% CI: 0.822-0.938). In order to evaluate the impact of the proposed method, performance comparison with several baseline methods was also performed. While limited by case volume and a few more factors, the results of this study can contribute to the potential development of an artificial intelligence system to diagnose other cancers without extensive region-level annotations.
| S-EPMC8203612 | biostudies-literature
Project description:ImportanceFor prostate cancer, Gleason grading of the biopsy specimen plays a pivotal role in determining case management. However, Gleason grading is associated with substantial interobserver variability, resulting in a need for decision support tools to improve the reproducibility of Gleason grading in routine clinical practice.ObjectiveTo evaluate the ability of a deep learning system (DLS) to grade diagnostic prostate biopsy specimens.Design, setting, and participantsThe DLS was evaluated using 752 deidentified digitized images of formalin-fixed paraffin-embedded prostate needle core biopsy specimens obtained from 3 institutions in the United States, including 1 institution not used for DLS development. To obtain the Gleason grade group (GG), each specimen was first reviewed by 2 expert urologic subspecialists from a multi-institutional panel of 6 individuals (years of experience: mean, 25 years; range, 18-34 years). A third subspecialist reviewed discordant cases to arrive at a majority opinion. To reduce diagnostic uncertainty, all subspecialists had access to an immunohistochemical-stained section and 3 histologic sections for every biopsied specimen. Their review was conducted from December 2018 to June 2019.Main outcomes and measuresThe frequency of the exact agreement of the DLS with the majority opinion of the subspecialists in categorizing each tumor-containing specimen as 1 of 5 categories: nontumor, GG1, GG2, GG3, or GG4-5. For comparison, the rate of agreement of 19 general pathologists' opinions with the subspecialists' majority opinions was also evaluated.ResultsFor grading tumor-containing biopsy specimens in the validation set (n = 498), the rate of agreement with subspecialists was significantly higher for the DLS (71.7%; 95% CI, 67.9%-75.3%) than for general pathologists (58.0%; 95% CI, 54.5%-61.4%) (P < .001). In subanalyses of biopsy specimens from an external validation set (n = 322), the Gleason grading performance of the DLS remained similar. For distinguishing nontumor from tumor-containing biopsy specimens (n = 752), the rate of agreement with subspecialists was 94.3% (95% CI, 92.4%-95.9%) for the DLS and similar at 94.7% (95% CI, 92.8%-96.3%) for general pathologists (P = .58).Conclusions and relevanceIn this study, the DLS showed higher proficiency than general pathologists at Gleason grading prostate needle core biopsy specimens and generalized to an independent institution. Future research is necessary to evaluate the potential utility of using the DLS as a decision support tool in clinical workflows and to improve the quality of prostate cancer grading for therapy decisions.
| S-EPMC7378872 | biostudies-literature
Project description:Background and objectiveGleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.MethodsTo capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.ResultsOn the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.ConclusionsWe presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading.
| S-EPMC8153074 | biostudies-literature
Project description:For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p?=?0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.
| S-EPMC6555810 | biostudies-literature
Project description:Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.
| S-EPMC6497079 | biostudies-literature
Project description:Despite revisions in 2005 and 2014, the Gleason prostate cancer (PCa) grading system still has major deficiencies. Combining of Gleason scores into a three-tiered grouping (6, 7, 8-10) is used most frequently for prognostic and therapeutic purposes. The lowest score, assigned 6, may be misunderstood as a cancer in the middle of the grading scale, and 3+4=7 and 4+3=7 are often considered the same prognostic group.To verify that a new grading system accurately produces a smaller number of grades with the most significant prognostic differences, using multi-institutional and multimodal therapy data.Between 2005 and 2014, 20,845 consecutive men were treated by radical prostatectomy at five academic institutions; 5501 men were treated with radiotherapy at two academic institutions.Outcome was based on biochemical recurrence (BCR). The log-rank test assessed univariable differences in BCR by Gleason score. Separate univariable and multivariable Cox proportional hazards used four possible categorizations of Gleason scores.In the surgery cohort, we found large differences in recurrence rates between both Gleason 3+4 versus 4+3 and Gleason 8 versus 9. The hazard ratios relative to Gleason score 6 were 1.9, 5.1, 8.0, and 11.7 for Gleason scores 3+4, 4+3, 8, and 9-10, respectively. These differences were attenuated in the radiotherapy cohort as a whole due to increased adjuvant or neoadjuvant hormones for patients with high-grade disease but were clearly seen in patients undergoing radiotherapy only. A five-grade group system had the highest prognostic discrimination for all cohorts on both univariable and multivariable analysis. The major limitation was the unavoidable use of prostate-specific antigen BCR as an end point as opposed to cancer-related death.The new PCa grading system has these benefits: more accurate grade stratification than current systems, simplified grading system of five grades, and lowest grade is 1, as opposed to 6, with the potential to reduce overtreatment of PCa.We looked at outcomes for prostate cancer (PCa) treated with radical prostatectomy or radiation therapy and validated a new grading system with more accurate grade stratification than current systems, including a simplified grading system of five grades and a lowest grade is 1, as opposed to 6, with the potential to reduce overtreatment of PCa.
| S-EPMC5002992 | biostudies-literature
Project description:Current prostate cancer risk classifications rely on clinicopathological parameters resulting in uncertainties for prognostication. To improve individual risk stratification, we examined the predictive value of selected proteins with respect to tumor heterogeneity and genomic instability. We assessed the degree of genomic instability in 50 radical prostatectomy specimens by DNA-Image-Cytometry and evaluated protein expression in related 199 tissue-microarray (TMA) cores. Immunohistochemical data of SATB1, SPIN1, TPM4, VIME and TBB5 were correlated with the degree of genomic instability, established clinical risk factors and overall survival. Genomic instability was associated with a GS ≥ 7 (p = 0.001) and worse overall survival (p = 0.008). A positive SATB1 expression was associated with a GS ≤ 6 (p = 0.040), genomic stability (p = 0.027), and was a predictor for increased overall survival (p = 0.023). High expression of SPIN1 was also associated with longer overall survival (p = 0.048) and lower preoperative PSA-values (p = 0.047). The combination of SATB1 expression, genomic instability, and GS lead to a novel Prostate Cancer Prediction Score (PCP-Score) which outperforms the current D'Amico et al. stratification for predicting overall survival. Low SATB1 expression, genomic instability and GS ≥ 7 were identified as markers for poor prognosis. Their combination overcomes current clinical risk stratification regimes.
| S-EPMC8712510 | biostudies-literature
Project description:Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P?=?0.0007?<?0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.
| S-EPMC5684419 | biostudies-literature
Project description:Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.
| S-EPMC8656605 | biostudies-literature