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Rani ND, Babu M. Improved rank-based recursive feature elimination method based ovarian cancer detection model via customized deep architecture. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108358. [PMID: 39191100 DOI: 10.1016/j.cmpb.2024.108358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/22/2024] [Accepted: 07/27/2024] [Indexed: 08/29/2024]
Abstract
BACKGROUND Ovarian cancer is often considered the most lethal gynecological cancer because it tends to be diagnosed at an advanced stage, leading to limited treatment options and poorer outcomes. Several factors contribute to the challenges in managing ovarian cancer, namely rapid metastasis, genetic factors, reproductive history, etc. This necessitates the prompt and precise diagnosis of ovarian cancer in order to carry out efficient treatment plans and give patients who are all impacted by OC the care and support they need. METHODS This CCLSTM model is suggested under four essential stages including preprocessing, feature extraction, feature selection and detection. Initially, the input data is preprocessed using Improved Two-step Data Normalization. Subsequently, features such as statistical, modified entropy, raw features and mutual information are extracted from the normalized data. Next, obtained features undergo the Improved Rank-based Recursive Feature Elimination method (IR-RFE) to select the most suitable features. Finally, the proposed CCLSTM model takes the selected features as input and provides a final detection outcome. RESULTS Furthermore, the performance of the proposed CCLSTM technique is examined through a thorough assessment using diverse analyses Additionally, the CCLSTM schemes show a sensitivity value of 0.948, whereas the sensitivity ratings for ALO-LSTM + ALOCNN, Bi-GRU, LSTM, RNN, KNN, CNN, and DCNN are 0.808, 0.893, 0.829, 0.851, 0.765, 0.872, and 0.893, respectively. CONCLUSION In the end, the development of CNN and the addition of LSTM technique have produced an ovarian cancer detection technique that is more accurate and consistent compared to other existing strategies.
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Affiliation(s)
- Namani Deepika Rani
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, Telangana, India.
| | - Mahesh Babu
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, Telangana, India
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2
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Li L, He L, Guo W, Ma J, Sun G, Ma H. PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation. PLoS One 2024; 19:e0299360. [PMID: 38557660 PMCID: PMC10984528 DOI: 10.1371/journal.pone.0299360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/09/2024] [Indexed: 04/04/2024] Open
Abstract
Ovarian cancer is a highly lethal malignancy in the field of oncology. Generally speaking, the segmentation of ovarian medical images is a necessary prerequisite for the diagnosis and treatment planning. Therefore, accurately segmenting ovarian tumors is of utmost importance. In this work, we propose a hybrid network called PMFFNet to improve the segmentation accuracy of ovarian tumors. The PMFFNet utilizes an encoder-decoder architecture. Specifically, the encoder incorporates the ViTAEv2 model to extract inter-layer multi-scale features from the feature pyramid. To address the limitation of fixed window size that hinders sufficient interaction of information, we introduce Varied-Size Window Attention (VSA) to the ViTAEv2 model to capture rich contextual information. Additionally, recognizing the significance of multi-scale features, we introduce the Multi-scale Feature Fusion Block (MFB) module. The MFB module enhances the network's capacity to learn intricate features by capturing both local and multi-scale information, thereby enabling more precise segmentation of ovarian tumors. Finally, in conjunction with our designed decoder, our model achieves outstanding performance on the MMOTU dataset. The results are highly promising, with the model achieving scores of 97.24%, 91.15%, and 87.25% in mACC, mIoU, and mDice metrics, respectively. When compared to several Unet-based and advanced models, our approach demonstrates the best segmentation performance.
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Affiliation(s)
- Lang Li
- School of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Liang He
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Wenjia Guo
- Cancer Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jing Ma
- School of Computer Science and Technology, Xinjiang University, Urumqi, Xinjiang, China
| | - Gang Sun
- Department of Breast and Thyroid Surgery, The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Cancer Center, Key Laboratory of Oncology of Xinjiang Uyghur Autonomous Region, Urumqi, Xinjiang, China
| | - Hongbing Ma
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
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3
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Wang Y, Lin W, Zhuang X, Wang X, He Y, Li L, Lyu G. Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review). Oncol Rep 2024; 51:46. [PMID: 38240090 PMCID: PMC10828921 DOI: 10.3892/or.2024.8705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.
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Affiliation(s)
- Yanli Wang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Weihong Lin
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiaoling Zhuang
- Department of Pathology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiali Wang
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
| | - Yifang He
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Luhong Li
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Guorong Lyu
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
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4
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Gadermayr M, Tschuchnig M. Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential. Comput Med Imaging Graph 2024; 112:102337. [PMID: 38228020 DOI: 10.1016/j.compmedimag.2024.102337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 12/04/2023] [Accepted: 01/09/2024] [Indexed: 01/18/2024]
Abstract
Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of digital pathology. However, a limitation is given by the fact that typical deep learning algorithms require (manual) annotations in addition to the large amounts of image data, to enable effective training. Multiple instance learning exhibits a powerful tool for training deep neural networks in a scenario without fully annotated data. These methods are particularly effective in the domain of digital pathology, due to the fact that labels for whole slide images are often captured routinely, whereas labels for patches, regions, or pixels are not. This potential resulted in a considerable number of publications, with the vast majority published in the last four years. Besides the availability of digitized data and a high motivation from the medical perspective, the availability of powerful graphics processing units exhibits an accelerator in this field. In this paper, we provide an overview of widely and effectively used concepts of (deep) multiple instance learning approaches and recent advancements. We also critically discuss remaining challenges as well as future potential.
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Affiliation(s)
- Michael Gadermayr
- Department of Information Technologies and Digitalisation, Salzburg University of Applied Sciences, Austria.
| | - Maximilian Tschuchnig
- Department of Information Technologies and Digitalisation, Salzburg University of Applied Sciences, Austria; Department of Artificial Intelligence and Human Interfaces, University of Salzburg, Austria
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Sadeghi MH, Sina S, Omidi H, Farshchitabrizi AH, Alavi M. Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities. Pol J Radiol 2024; 89:e30-e48. [PMID: 38371888 PMCID: PMC10867948 DOI: 10.5114/pjr.2024.134817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 12/27/2023] [Indexed: 02/20/2024] Open
Abstract
Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.
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Affiliation(s)
| | - Sedigheh Sina
- Shiraz University, Shiraz, Iran
- Radiation Research Center, Shiraz University, Shiraz, Iran
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6
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Bourgade R, Rabilloud N, Perennec T, Pécot T, Garrec C, Guédon AF, Delnatte C, Bézieau S, Lespagnol A, de Tayrac M, Henno S, Sagan C, Toquet C, Mosnier JF, Kammerer-Jacquet SF, Loussouarn D. Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images. Mod Pathol 2023; 36:100304. [PMID: 37580018 DOI: 10.1016/j.modpat.2023.100304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/15/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023]
Abstract
BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sensitivity of high-grade ovarian cancer (HGOC) to poly(ADP-ribose) polymerase inhibitors. However, their testing by next-generation sequencing is costly and time-consuming and can be affected by various preanalytical factors. In this study, we present a deep learning classifier for BRCA mutational status prediction from hematoxylin-eosin-safran-stained whole slide images (WSI) of HGOC. We constituted the OvarIA cohort composed of 867 patients with HGOC with known BRCA somatic mutational status from 2 different pathology departments. We first developed a tumor segmentation model according to dynamic sampling and then trained a visual representation encoder with momentum contrastive learning on the predicted tumor tiles. We finally trained a BRCA classifier on more than a million tumor tiles in multiple instance learning with an attention-based mechanism. The tumor segmentation model trained on 8 WSI obtained a dice score of 0.915 and an intersection-over-union score of 0.847 on a test set of 50 WSI, while the BRCA classifier achieved the state-of-the-art area under the receiver operating characteristic curve of 0.739 in 5-fold cross-validation and 0.681 on the testing set. An additional multiscale approach indicates that the relevant information for predicting BRCA mutations is located more in the tumor context than in the cell morphology. Our results suggest that BRCA somatic mutations have a discernible phenotypic effect that could be detected by deep learning and could be used as a prescreening tool in the future.
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Affiliation(s)
- Raphaël Bourgade
- Department of Pathology, University Hospital of Nantes, Nantes, France.
| | - Noémie Rabilloud
- Laboratoire du Traitement du Signal et de l'Image - Inserm U1099, University of Rennes, Rennes, France
| | - Tanguy Perennec
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest Nantes, Saint-Herblain, France
| | - Thierry Pécot
- Facility for Artificial Intelligence and Image Analysis (FAIIA), Biosit UAR 3480 CNRS-US18 INSERM, University of Rennes, Rennes, France
| | - Céline Garrec
- Department of Medical Genetics, University Hospital of Nantes, Nantes, France
| | - Alexis F Guédon
- National Institute of Health and Medical Research, Pierre Louis Institute of Epidemiology and Public Health, Sorbonne University, Paris, France
| | - Capucine Delnatte
- Department of Medical Genetics, University Hospital of Nantes, Nantes, France
| | - Stéphane Bézieau
- Department of Medical Genetics, University Hospital of Nantes, Nantes, France
| | - Alexandra Lespagnol
- Department of Molecular Genetics and Genomics, University Hospital of Rennes, Rennes, France
| | - Marie de Tayrac
- Department of Molecular Genetics and Genomics, University Hospital of Rennes, Rennes, France
| | - Sébastien Henno
- Department of Pathology, University Hospital of Rennes, Rennes, France
| | - Christine Sagan
- Department of Pathology, University Hospital of Nantes, Nantes, France
| | - Claire Toquet
- Department of Pathology, University Hospital of Nantes, Nantes, France
| | | | - Solène-Florence Kammerer-Jacquet
- Laboratoire du Traitement du Signal et de l'Image - Inserm U1099, University of Rennes, Rennes, France; Department of Pathology, University Hospital of Rennes, Rennes, France
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7
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Breen J, Allen K, Zucker K, Adusumilli P, Scarsbrook A, Hall G, Orsi NM, Ravikumar N. Artificial intelligence in ovarian cancer histopathology: a systematic review. NPJ Precis Oncol 2023; 7:83. [PMID: 37653025 PMCID: PMC10471607 DOI: 10.1038/s41698-023-00432-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/01/2023] [Indexed: 09/02/2023] Open
Abstract
This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.
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Affiliation(s)
- Jack Breen
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
| | - Katie Allen
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
| | - Kieran Zucker
- Leeds Cancer Centre, St James's University Hospital, Leeds, UK
| | - Pratik Adusumilli
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
- Department of Radiology, St James's University Hospital, Leeds, UK
| | - Andrew Scarsbrook
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
- Department of Radiology, St James's University Hospital, Leeds, UK
| | - Geoff Hall
- Leeds Cancer Centre, St James's University Hospital, Leeds, UK
| | - Nicolas M Orsi
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
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8
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Couture HD. Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review. J Pers Med 2022; 12:2022. [PMID: 36556243 PMCID: PMC9784641 DOI: 10.3390/jpm12122022] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/26/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.
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