4.4 Review

Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Urology & Nephrology

Deep Learning-based Recurrence Prediction in Patients with Non-muscle-invasive Bladder Cancer

Marit Lucas et al.

Summary: This study combines digital histopathology slides with clinical data using deep learning to predict the recurrence-free survival of patients with non-muscle-invasive bladder cancer (NMIBC). The results show that the deep learning-based model, which combines digital histopathology slides and clinical data, performs better in predicting recurrence compared to models using clinical data or image data only.

EUROPEAN UROLOGY FOCUS (2022)

Article Computer Science, Interdisciplinary Applications

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

Richard J. Chen et al.

Summary: This study proposes an interpretable strategy for multimodal fusion of histology image and genomic features for survival outcome prediction. The results on glioma and clear cell renal cell carcinoma datasets demonstrate that this approach improves the prognostic determinations.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

Article Urology & Nephrology

Assessment of predictors of renal cell carcinoma progression after nephrectomy at short- and medium-term follow-up and implication on surveillance protocols

Davide Perri et al.

Summary: In patients with clear-cell RCC, high grade (G3-G4), high stage (pT3-4), and PSMs are independent predictors of progression after surgery. Lower stage and grade renal cancers primarily progress in the abdominal sites and may require less frequent extra-abdominal imaging compared to higher risk and more aggressive tumors.

MINERVA UROLOGY AND NEPHROLOGY (2022)

Article Pathology

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

Wouter Bulten et al.

Summary: The Gleason score is crucial for prostate cancer prognosis, and AI systems based on deep learning can help improve pathologist performance in grading. However, the performance of AI systems may degrade in the presence of artifacts. Integrating pathologists' expertise with AI feedback can lead to better results.

MODERN PATHOLOGY (2021)

Article Oncology

Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma

Siteng Chen et al.

Summary: This study developed a novel computational recognition technology using machine learning methods for accurate diagnosis and prognosis prediction of ccRCC patients. The results showed high diagnostic accuracy in the training, test, and external validation cohorts, demonstrating the potential clinical use of this machine learning histopathological image signature for ccRCC.

INTERNATIONAL JOURNAL OF CANCER (2021)

Article Urology & Nephrology

European Association of Urology (EAU) Prognostic Factor Risk Groups for Non-muscle-invasive Bladder Cancer (NMIBC) Incorporating the WHO 2004/2016 and WHO 1973 Classification Systems for Grade: An Update from the EAU NMIBC Guidelines Panel

Richard J. Sylvester et al.

Summary: This study updated the European Association of Urology prognostic factor risk groups for non-muscle-invasive bladder cancer, incorporating the WHO 2004/2016 and 1973 grading classifications and identifying a new very high-risk group. This can help urologists better tailor patients' treatment and follow-up plans.

EUROPEAN UROLOGY (2021)

Article Computer Science, Interdisciplinary Applications

The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies

Aniek F. Markus et al.

Summary: This paper discusses the issue of explainable AI in the healthcare domain, proposes a framework for choosing explainable AI methods, and highlights the lack of evaluation metrics in some aspects.

JOURNAL OF BIOMEDICAL INFORMATICS (2021)

Article Urology & Nephrology

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer

Frederik Wessels et al.

Summary: A new digital biomarker based on CNN analysis of primary tumor tissue was developed for predicting lymph node metastasis in prostate cancer patients. With 10 trained models, an average AUROC of 0.68 and balanced accuracy of 61.37% was achieved. The CNN probability score and lymphovascular invasion were identified as independent predictors for LNM.

BJU INTERNATIONAL (2021)

Article Oncology

Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma

Eliana Marostica et al.

Summary: The study demonstrates that convolutional neural networks can accurately diagnose renal cancers, predict patients' genomic profiles and prognosis, and identify genomic variations, showcasing the potential for integrating diverse data modalities in clinical research.

CLINICAL CANCER RESEARCH (2021)

Article Urology & Nephrology

Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study

Ohad Kott et al.

Summary: A state-of-the-art deep learning algorithm was developed for the histopathologic diagnosis and Gleason grading of prostate biopsy specimens, achieving 91.5% accuracy in coarse classification and 85.4% accuracy in fine classification. The algorithm showed excellent performance with high sensitivity and specificity, though limitations include the small sample size and the need for external validation.

EUROPEAN UROLOGY FOCUS (2021)

Article Urology & Nephrology

Comparison between minimally-invasive partial and radical nephrectomy for the treatment of clinical T2 renal masses: results of a 10-year study in a tertiary care center

Daniele Amparore et al.

Summary: The study indicates that in experienced hands, minimally-invasive partial nephrectomy is feasible for cT2 renal tumors, providing perioperative and oncological safety profiles comparable to radical nephrectomy, with advantages in terms of functional outcomes.

MINERVA UROLOGY AND NEPHROLOGY (2021)

Review Urology & Nephrology

Artificial intelligence and neural networks in urology: current clinical applications

Enrico Checcucci et al.

MINERVA UROLOGICA E NEFROLOGICA (2020)

Article Biology

Concept attribution: Explaining CNN decisions to physicians

M. Graziani et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

Article Urology & Nephrology

Patterns of positive surgical margins after open radical prostatectomy and their association with clinical recurrence

Lorenzo Bianchi et al.

MINERVA UROLOGICA E NEFROLOGICA (2020)

Article Oncology

Pan-cancer image-based detection of clinically actionable genetic alterations

Jakob Nikolas Kather et al.

NATURE CANCER (2020)

Article Urology & Nephrology

European Association of Urology Guidelines on Renal Cell Carcinoma: The 2019 Update

Borje Ljungberg et al.

EUROPEAN UROLOGY (2019)

Article Oncology

Digital pathology and artificial intelligence

Muhammad Khalid Khan Niazi et al.

LANCET ONCOLOGY (2019)

Article Urology & Nephrology

Augmented Bladder Tumor Detection Using Deep Learning

Eugene Shkolyar et al.

EUROPEAN UROLOGY (2019)

Article Oncology

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology

Kaustav Bera et al.

NATURE REVIEWS CLINICAL ONCOLOGY (2019)

Article Medicine, General & Internal

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Robert F. Wolff et al.

ANNALS OF INTERNAL MEDICINE (2019)

Article Multidisciplinary Sciences

Automated acquisition of explainable knowledge from unannotated histopathology images

Yoichiro Yamamoto et al.

NATURE COMMUNICATIONS (2019)

Article Health Care Sciences & Services

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Kunal Nagpal et al.

NPJ DIGITAL MEDICINE (2019)

Article Multidisciplinary Sciences

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

Eirini Arvaniti et al.

SCIENTIFIC REPORTS (2018)

Review Health Care Sciences & Services

Medical Image Analysis using Convolutional Neural Networks: A Review

Syed Muhammad Anwar et al.

JOURNAL OF MEDICAL SYSTEMS (2018)

Review Health Care Sciences & Services

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

David Moher et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2009)

Article Urology & Nephrology

Personalized Prediction of Tumor Response and Cancer Progression on Prostate Needle Biopsy

Michael J. Donovan et al.

JOURNAL OF UROLOGY (2009)

Article Computer Science, Information Systems

A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence

P Spyridonos et al.

MEDICAL INFORMATICS AND THE INTERNET IN MEDICINE (2002)