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Lee J, Narang S, Martinez J, Rao G, Rao A. Association of graph-based spatial features with overall survival status of glioblastoma patients. Sci Rep 2023; 13:17046. [PMID: 37813981 PMCID: PMC10562480 DOI: 10.1038/s41598-023-44353-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 10/06/2023] [Indexed: 10/11/2023] Open
Abstract
Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma.
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Affiliation(s)
- Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Shivali Narang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Juan Martinez
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ganesh Rao
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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2
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Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol 2023; 18:109. [PMID: 37784122 PMCID: PMC10546747 DOI: 10.1186/s13000-023-01375-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/21/2023] [Indexed: 10/04/2023] Open
Abstract
Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.
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Affiliation(s)
- Saba Shafi
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA.
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3
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Liu Y, Han D, Parwani AV, Li Z. Applications of Artificial Intelligence in Breast Pathology. Arch Pathol Lab Med 2023; 147:1003-1013. [PMID: 36800539 DOI: 10.5858/arpa.2022-0457-ra] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2022] [Indexed: 02/19/2023]
Abstract
CONTEXT.— Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology. OBJECTIVE.— To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes. DATA SOURCES.— We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience. CONCLUSIONS.— With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.
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Affiliation(s)
- Yueping Liu
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
| | - Dandan Han
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
| | - Anil V Parwani
- The Department of Pathology, The Ohio State University, Columbus (Parwani, Li)
| | - Zaibo Li
- From the Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China (Liu, Han)
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4
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Choi S, Cho SI, Jung W, Lee T, Choi SJ, Song S, Park G, Park S, Ma M, Pereira S, Yoo D, Shin S, Ock CY, Kim S. Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer. NPJ Breast Cancer 2023; 9:71. [PMID: 37648694 PMCID: PMC10469174 DOI: 10.1038/s41523-023-00577-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/17/2023] [Indexed: 09/01/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | | | | | - Su Jin Choi
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | | | | | | | - Minuk Ma
- Lunit Inc, Seoul, Republic of Korea
| | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
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5
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Thagaard J, Broeckx G, Page DB, Jahangir CA, Verbandt S, Kos Z, Gupta R, Khiroya R, Abduljabbar K, Acosta Haab G, Acs B, Akturk G, Almeida JS, Alvarado‐Cabrero I, Amgad M, Azmoudeh‐Ardalan F, Badve S, Baharun NB, Balslev E, Bellolio ER, Bheemaraju V, Blenman KRM, Botinelly Mendonça Fujimoto L, Bouchmaa N, Burgues O, Chardas A, Chon U Cheang M, Ciompi F, Cooper LAD, Coosemans A, Corredor G, Dahl AB, Dantas Portela FL, Deman F, Demaria S, Doré Hansen J, Dudgeon SN, Ebstrup T, Elghazawy M, Fernandez‐Martín C, Fox SB, Gallagher WM, Giltnane JM, Gnjatic S, Gonzalez‐Ericsson PI, Grigoriadis A, Halama N, Hanna MG, Harbhajanka A, Hart SN, Hartman J, Hauberg S, Hewitt S, Hida AI, Horlings HM, Husain Z, Hytopoulos E, Irshad S, Janssen EAM, Kahila M, Kataoka TR, Kawaguchi K, Kharidehal D, Khramtsov AI, Kiraz U, Kirtani P, Kodach LL, Korski K, Kovács A, Laenkholm A, Lang‐Schwarz C, Larsimont D, Lennerz JK, Lerousseau M, Li X, Ly A, Madabhushi A, Maley SK, Manur Narasimhamurthy V, Marks DK, McDonald ES, Mehrotra R, Michiels S, Minhas FUAA, Mittal S, Moore DA, Mushtaq S, Nighat H, Papathomas T, Penault‐Llorca F, Perera RD, Pinard CJ, Pinto‐Cardenas JC, Pruneri G, Pusztai L, Rahman A, Rajpoot NM, Rapoport BL, Rau TT, Reis‐Filho JS, Ribeiro JM, Rimm D, Roslind A, Vincent‐Salomon A, Salto‐Tellez M, Saltz J, Sayed S, Scott E, Siziopikou KP, Sotiriou C, Stenzinger A, Sughayer MA, Sur D, Fineberg S, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson EA, Tramm T, Tran WT, van der Laak J, van Diest PJ, Verghese GE, Viale G, Vieth M, Wahab N, Walter T, Waumans Y, Wen HY, Yang W, Yuan Y, Zin RM, Adams S, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Salgado R, Specht Stovgaard E. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer. J Pathol 2023; 260:498-513. [PMID: 37608772 PMCID: PMC10518802 DOI: 10.1002/path.6155] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/07/2023] [Indexed: 08/24/2023]
Abstract
The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Jeppe Thagaard
- Technical University of DenmarkKongens LyngbyDenmark
- Visiopharm A/SHørsholmDenmark
| | - Glenn Broeckx
- Department of PathologyGZA‐ZNA HospitalsAntwerpBelgium
- Centre for Oncological Research (CORE), MIPPRO, Faculty of MedicineAntwerp UniversityAntwerpBelgium
| | - David B Page
- Earle A Chiles Research InstituteProvidence Cancer InstitutePortlandORUSA
| | - Chowdhury Arif Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway InstituteUniversity College DublinDublinIreland
| | - Sara Verbandt
- Digestive Oncology, Department of OncologyKU LeuvenLeuvenBelgium
| | - Zuzana Kos
- Department of Pathology and Laboratory MedicineBC Cancer Vancouver Centre, University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Rajarsi Gupta
- Department of Biomedical InformaticsStony Brook UniversityStony BrookNYUSA
| | - Reena Khiroya
- Department of Cellular PathologyUniversity College Hospital LondonLondonUK
| | | | | | - Balazs Acs
- Department of Oncology and PathologyKarolinska InstitutetStockholmSweden
- Department of Clinical Pathology and Cancer DiagnosticsKarolinska University HospitalStockholmSweden
| | - Guray Akturk
- Translational Molecular Biomarkers, Merck & Co IncRahwayNJUSA
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics (DCEG)National Cancer Institute (NCI)Rockville, MDUSA
| | | | - Mohamed Amgad
- Department of PathologyNorthwestern University Feinberg School of MedicineChicagoILUSA
| | | | - Sunil Badve
- Department of Pathology and Laboratory Medicine, Emory University School of MedicineEmory University Winship Cancer InstituteAtlantaGAUSA
| | | | - Eva Balslev
- Department of PathologyHerlev and Gentofte HospitalHerlevDenmark
| | - Enrique R Bellolio
- Departamento de Anatomía Patológica, Facultad de MedicinaUniversidad de La FronteraTemucoChile
| | | | - Kim RM Blenman
- Department of Internal Medicine Section of Medical Oncology and Yale Cancer CenterYale School of MedicineNew HavenCTUSA
- Department of Computer ScienceYale School of Engineering and Applied ScienceNew HavenCTUSA
| | | | - Najat Bouchmaa
- Institute of Biological Sciences, Faculty of Medical SciencesMohammed VI Polytechnic University (UM6P)Ben‐GuerirMorocco
| | - Octavio Burgues
- Pathology DepartmentHospital Cliníco Universitario de Valencia/InclivaValenciaSpain
| | - Alexandros Chardas
- Department of Pathobiology & Population SciencesThe Royal Veterinary CollegeLondonUK
| | - Maggie Chon U Cheang
- Head of Integrative Genomics Analysis in Clinical Trials, ICR‐CTSU, Division of Clinical StudiesThe Institute of Cancer ResearchLondonUK
| | - Francesco Ciompi
- Radboud University Medical CenterDepartment of PathologyNijmegenThe Netherlands
| | - Lee AD Cooper
- Department of PathologyNorthwestern Feinberg School of MedicineChicagoILUSA
| | - An Coosemans
- Department of Oncology, Laboratory of Tumor Immunology and ImmunotherapyKU LeuvenLeuvenBelgium
| | - Germán Corredor
- Biomedical Engineering DepartmentEmory UniversityAtlantaGAUSA
| | - Anders B Dahl
- Technical University of DenmarkKongens LyngbyDenmark
| | | | | | - Sandra Demaria
- Department of Radiation OncologyWeill Cornell MedicineNew YorkNYUSA
- Department of Pathology and Laboratory MedicineWeill Cornell MedicineNew YorkNYUSA
| | | | - Sarah N Dudgeon
- Conputational Biology and BioinformaticsYale UniversityNew HavenCTUSA
| | | | | | - Claudio Fernandez‐Martín
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN‐techUniversitat Politècnica de ValènciaValenciaSpain
| | - Stephen B Fox
- Pathology, Peter MacCallum Cancer Centre and Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVictoriaAustralia
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway InstituteUniversity College DublinDublinIreland
| | | | - Sacha Gnjatic
- Department of Oncological Sciences, Medicine Hem/Onc, and Pathology, Tisch Cancer Institute – Precision Immunology InstituteIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | | | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Niels Halama
- Department of Translational ImmunotherapyGerman Cancer Research CenterHeidelbergGermany
| | - Matthew G Hanna
- Department of PathologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
| | | | - Steven N Hart
- Department of Laboratory Medicine and PathologyMayo ClinicRochester, MNUSA
| | - Johan Hartman
- Department of Oncology and PathologyKarolinska InstitutetStockholmSweden
- Department of Clinical Pathology and Cancer DiagnosticsKarolinska University HospitalStockholmSweden
| | - Søren Hauberg
- Technical University of DenmarkKongens LyngbyDenmark
| | - Stephen Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer InstituteNational Institutes of HealthBethesdaMDUSA
| | - Akira I Hida
- Department of PathologyMatsuyama Shimin HospitalMatsuyamaJapan
| | - Hugo M Horlings
- Division of PathologyNetherlands Cancer Institute (NKI)AmsterdamThe Netherlands
| | | | | | - Sheeba Irshad
- King's College London & Guy's & St Thomas’ NHS TrustLondonUK
| | - Emiel AM Janssen
- Department of PathologyStavanger University HospitalStavangerNorway
- Department of Chemistry, Bioscience and Environmental TechnologyUniversity of StavangerStavangerNorway
| | | | | | - Kosuke Kawaguchi
- Department of Breast SurgeryKyoto University Graduate School of MedicineKyotoJapan
| | | | - Andrey I Khramtsov
- Department of Pathology and Laboratory MedicineAnn & Robert H. Lurie Children's Hospital of ChicagoChicagoILUSA
| | - Umay Kiraz
- Department of PathologyStavanger University HospitalStavangerNorway
- Department of Chemistry, Bioscience and Environmental TechnologyUniversity of StavangerStavangerNorway
| | - Pawan Kirtani
- Department of HistopathologyAakash Healthcare Super Speciality HospitalNew DelhiIndia
| | - Liudmila L Kodach
- Department of PathologyNetherlands Cancer Institute – Antoni van Leeuwenhoek HospitalAmsterdamThe Netherlands
| | - Konstanty Korski
- Data, Analytics and Imaging, Product DevelopmentF. Hoffmann‐La Roche AGBaselSwitzerland
| | - Anikó Kovács
- Department of Clinical PathologySahlgrenska University HospitalGothenburgSweden
- Institute of Biomedicine, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Anne‐Vibeke Laenkholm
- Department of Surgical PathologyZealand University HospitalRoskildeDenmark
- Department of Surgical PathologyUniversity of CopenhagenCopenhagenDenmark
| | - Corinna Lang‐Schwarz
- Institute of Pathology, Klinikum Bayreuth GmbHFriedrich‐Alexander‐University Erlangen‐NurembergBayreuthGermany
| | - Denis Larsimont
- Institut Jules BordetUniversité Libre de BruxellesBrusselsBelgium
| | - Jochen K Lennerz
- Center for Integrated DiagnosticsMassachusetts General Hospital/Harvard Medical SchoolBostonMAUSA
| | - Marvin Lerousseau
- Centre for Computational Biology (CBIO)Mines Paris, PSL UniversityParisFrance
- Institut CuriePSL UniversityParisFrance
- INSERMParisFrance
| | - Xiaoxian Li
- Department of Pathology and Laboratory MedicineEmory UniversityAtlantaGAUSA
| | - Amy Ly
- Department of PathologyMassachusetts General HospitalBostonMAUSA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Radiology and Imaging Sciences, Biomedical Informatics, PathologyGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
| | - Sai K Maley
- NRG Oncology/NSABP FoundationPittsburghPAUSA
| | | | | | - Elizabeth S McDonald
- Breast Cancer Translational Research GroupUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Ravi Mehrotra
- Indian Cancer Genomic AtlasPuneIndia
- Centre for Health, Innovation and Policy FoundationNoidaIndia
| | - Stefan Michiels
- Office of Biostatistics and Epidemiology, Gustave Roussy, Oncostat U1018, InsermUniversity Paris‐Saclay, Ligue Contre le Cancer labeled TeamVillejuifFrance
| | - Fayyaz ul Amir Afsar Minhas
- Tissue Image Analytics Centre, Warwick Cancer Research Centre, PathLAKE Consortium, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Shachi Mittal
- Department of Chemical Engineering, Department of Laboratory Medicine and PathologyUniversity of WashingtonSeattle, WAUSA
| | - David A Moore
- CRUK Lung Cancer Centre of Excellence, UCL and Cellular Pathology DepartmentUCLHLondonUK
| | - Shamim Mushtaq
- Department of BiochemistryZiauddin UniversityKarachiPakistan
| | - Hussain Nighat
- Pathology and Laboratory MedicineAll India Institute of Medical sciencesRaipurIndia
| | - Thomas Papathomas
- Institute of Metabolism and Systems ResearchUniversity of BirminghamBirminghamUK
- Department of Clinical PathologyDrammen Sykehus, Vestre Viken HFDrammenNorway
| | - Frederique Penault‐Llorca
- Centre Jean Perrin, Université Clermont Auvergne, INSERM, U1240 Imagerie Moléculaire et Stratégies ThéranostiquesClermont FerrandFrance
| | - Rashindrie D Perera
- School of Electrical, Mechanical and Infrastructure EngineeringUniversity of MelbourneMelbourneVictoriaAustralia
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Christopher J Pinard
- Radiogenomics LaboratorySunnybrook Health Sciences CentreTorontoOntarioCanada
- Department of Clinical Studies, Ontario Veterinary CollegeUniversity of GuelphGuelphOntarioCanada
- Department of OncologyLakeshore Animal Health PartnersMississaugaOntarioCanada
- Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE‐AI)University of GuelphGuelphOntarioCanada
| | | | - Giancarlo Pruneri
- Department of Pathology and Laboratory MedicineFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
- Faculty of Medicine and SurgeryUniversity of MilanMilanItaly
| | - Lajos Pusztai
- Yale Cancer CenterYale UniversityNew HavenCTUSA
- Department of Medical Oncology, Yale School of MedicineYale UniversityNew HavenCTUSA
| | - Arman Rahman
- UCD School of Biomolecular and Biomedical Science, UCD Conway InstituteUniversity College DublinDublinIreland
| | | | - Bernardo Leon Rapoport
- The Medical Oncology Centre of RosebankJohannesburgSouth Africa
- Department of Immunology, Faculty of Health SciencesUniversity of PretoriaPretoriaSouth Africa
| | - Tilman T Rau
- Institute of PathologyUniversity Hospital Düsseldorf and Heinrich‐Heine‐University DüsseldorfDüsseldorfGermany
| | - Jorge S Reis‐Filho
- Department of Pathology and Laboratory MedicineMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Joana M Ribeiro
- Département de Médecine OncologiqueGustave RoussyVillejuifFrance
| | - David Rimm
- Department of PathologyYale University School of MedicineNew HavenCTUSA
- Department of MedicineYale University School of MedicineNew HavenCTUSA
| | - Anne Roslind
- Department of PathologyHerlev and Gentofte HospitalHerlevDenmark
| | - Anne Vincent‐Salomon
- Department of Diagnostic and Theranostic Medicine, Institut CurieUniversity Paris‐Sciences et LettresParisFrance
| | - Manuel Salto‐Tellez
- Integrated Pathology UnitThe Institute of Cancer ResearchLondonUK
- Precision Medicine CentreQueen's University BelfastBelfastUK
| | - Joel Saltz
- Department of Biomedical InformaticsStony Brook UniversityStony BrookNYUSA
| | - Shahin Sayed
- Department of PathologyAga Khan UniversityNairobiKenya
| | - Ely Scott
- Translational PathologyTranslational Sciences and Diagnostics/Translational Medicine/R&D, Bristol Myers SquibbPrincetonNJUSA
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast PathologyNorthwestern University Feinberg School of MedicineChicagoILUSA
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory J.‐C. Heuson, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (HUB)Université Libre de Bruxelles (ULB)BrusselsBelgium
- Medical Oncology Department, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (HUB)Université Libre de Bruxelles (ULB)BrusselsBelgium
| | - Albrecht Stenzinger
- Institute of PathologyUniversity Hospital HeidelbergHeidelbergGermany
- Centers for Personalized Medicine (ZPM)HeidelbergGermany
| | | | - Daniel Sur
- Department of Medical OncologyUniversity of Medicine and Pharmacy “Iuliu Hatieganu”Cluj‐NapocaRomania
| | - Susan Fineberg
- Montefiore Medical CenterBronxNYUSA
- Albert Einstein College of MedicineBronxNYUSA
| | - Fraser Symmans
- University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | | | | | - Sabine Tejpar
- Digestive Oncology, Department of OncologyKU LeuvenLeuvenBelgium
| | - Jonas Teuwen
- AI for Oncology Lab, The Netherlands Cancer InstituteAmsterdamThe Netherlands
| | | | - Trine Tramm
- Department of PathologyAarhus University HospitalAarhusDenmark
- Institute of Clinical MedicineAarhus UniversityAarhusDenmark
| | - William T Tran
- Department of Radiation OncologyUniversity of Toronto and Sunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Jeroen van der Laak
- Department of PathologyRadboud University Medical CenterNijmegenThe Netherlands
| | - Paul J van Diest
- Department of PathologyUniversity Medical Center UtrechtThe Netherlands
- Johns Hopkins Oncology CenterBaltimoreMDUSA
| | - Gregory E Verghese
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
- The Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Giuseppe Viale
- Department of PathologyEuropean Institute of OncologyMilanItaly
- Department of PathologyUniversity of MilanMilanItaly
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth GmbHFriedrich‐Alexander‐University Erlangen‐NurembergBayreuthGermany
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Thomas Walter
- Centre for Computational Biology (CBIO)Mines Paris, PSL UniversityParisFrance
- Institut CuriePSL UniversityParisFrance
- INSERMParisFrance
| | | | - Hannah Y Wen
- Department of PathologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
| | - Wentao Yang
- Fudan Medical University Shanghai Cancer CenterShanghaiPR China
| | - Yinyin Yuan
- Department of Translational Molecular Pathology, Division of Pathology and Laboratory MedicineThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Reena Md Zin
- Department of Pathology, Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia
| | - Sylvia Adams
- Perlmutter Cancer CenterNYU Langone HealthNew YorkNYUSA
- Department of MedicineNYU Grossman School of MedicineManhattanNYUSA
| | | | - Sibylle Loibl
- Department of Medicine and ResearchGerman Breast GroupNeu‐IsenburgGermany
| | - Carsten Denkert
- Institut für PathologiePhilipps‐Universität Marburg und Universitätsklinikum MarburgMarburgGermany
| | - Peter Savas
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- The Sir Peter MacCallum Department of Medical OncologyUniversity of MelbourneMelbourneVictoriaAustralia
| | - Sherene Loi
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- The Sir Peter MacCallum Department of Medical OncologyUniversity of MelbourneMelbourneVictoriaAustralia
| | - Roberto Salgado
- Department of PathologyGZA‐ZNA HospitalsAntwerpBelgium
- Division of Cancer ResearchPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Elisabeth Specht Stovgaard
- Department of PathologyHerlev and Gentofte HospitalHerlevDenmark
- Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
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6
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Park HS, Chong Y, Lee Y, Yim K, Seo KJ, Hwang G, Kim D, Gong G, Cho NH, Yoo CW, Choi HJ. Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid. Cells 2023; 12:1847. [PMID: 37508511 PMCID: PMC10377793 DOI: 10.3390/cells12141847] [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: 06/19/2023] [Revised: 07/07/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer. This study includes a total of 569 cytological slides of malignant pleural effusion of metastatic breast cancer from various institutions. We extracted 34,221 augmented image patches from whole-slide images and trained and validated a deep convolutional neural network model (DCNN) (Inception-ResNet-V2) with the images. Using this model, we classified 845 randomly selected patches, which were reviewed by three pathologists to compare their accuracy. The DCNN model outperforms the pathologists by demonstrating higher accuracy, sensitivity, and specificity compared to the pathologists (81.1% vs. 68.7%, 95.0% vs. 72.5%, and 98.6% vs. 88.9%, respectively). The pathologists reviewed the discordant cases of DCNN. After re-examination, the average accuracy, sensitivity, and specificity of the pathologists improved to 87.9, 80.2, and 95.7%, respectively. This study shows that DCNN can accurately diagnose malignant pleural effusion cytology in breast cancer and has the potential to support pathologists.
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Affiliation(s)
- Hong Sik Park
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Yosep Chong
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Yujin Lee
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Kwangil Yim
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Kyung Jin Seo
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Gisu Hwang
- AI Team, DeepNoid Inc., Seoul 08376, Republic of Korea
| | - Dahyeon Kim
- AI Team, DeepNoid Inc., Seoul 08376, Republic of Korea
| | - Gyungyub Gong
- Department of Pathology, Asan Medical Center, Seoul 05505, Republic of Korea
| | - Nam Hoon Cho
- Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Chong Woo Yoo
- Department of Pathology, National Cancer Center, Ilsan, Goyang-si 10408, Gyeonggi-do, Republic of Korea
| | - Hyun Joo Choi
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
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7
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Lother D, Robert M, Elwood E, Smith S, Tunariu N, Johnston SRD, Parton M, Bhaludin B, Millard T, Downey K, Sharma B. Imaging in metastatic breast cancer, CT, PET/CT, MRI, WB-DWI, CCA: review and new perspectives. Cancer Imaging 2023; 23:53. [PMID: 37254225 DOI: 10.1186/s40644-023-00557-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Breast cancer is the most frequent cancer in women and remains the second leading cause of death in Western countries. It represents a heterogeneous group of diseases with diverse tumoral behaviour, treatment responsiveness and prognosis. While major progress in diagnosis and treatment has resulted in a decline in breast cancer-related mortality, some patients will relapse and prognosis in this cohort of patients remains poor. Treatment is determined according to tumor subtype; primarily hormone receptor status and HER2 expression. Menopausal status and site of disease relapse are also important considerations in treatment protocols. MAIN BODY Staging and repeated evaluation of patients with metastatic breast cancer are central to the accurate assessment of disease extent at diagnosis and during treatment; guiding ongoing clinical management. Advances have been made in the diagnostic and therapeutic fields, particularly with new targeted therapies. In parallel, oncological imaging has evolved exponentially with the development of functional and anatomical imaging techniques. Consistent, reproducible and validated methods of assessing response to therapy is critical in effectively managing patients with metastatic breast cancer. CONCLUSION Major progress has been made in oncological imaging over the last few decades. Accurate disease assessment at diagnosis and during treatment is important in the management of metastatic breast cancer. CT (and BS if appropriate) is generally widely available, relatively cheap and sufficient in many cases. However, several additional imaging modalities are emerging and can be used as adjuncts, particularly in pregnancy or other diagnostically challenging cases. Nevertheless, no single imaging technique is without limitation. The authors have evaluated the vast array of imaging techniques - individual, combined parametric and multimodal - that are available or that are emerging in the management of metastatic breast cancer. This includes WB DW-MRI, CCA, novel PET breast cancer-epitope specific radiotracers and radiogenomics.
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Affiliation(s)
| | - Marie Robert
- Institut de Cancérologie de l'Ouest, St Herblain, France
| | | | - Sam Smith
- The Royal Marsden Hospital, London & Sutton, UK
| | - Nina Tunariu
- The Royal Marsden Hospital, London & Sutton, UK
- The Institute of Cancer Research (ICR), London & Sutton, UK
| | - Stephen R D Johnston
- The Royal Marsden Hospital, London & Sutton, UK
- The Institute of Cancer Research (ICR), London & Sutton, UK
| | | | | | | | - Kate Downey
- The Royal Marsden Hospital, London & Sutton, UK
- The Institute of Cancer Research (ICR), London & Sutton, UK
| | - Bhupinder Sharma
- The Royal Marsden Hospital, London & Sutton, UK.
- The Institute of Cancer Research (ICR), London & Sutton, UK.
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8
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Verdicchio M, Brancato V, Cavaliere C, Isgrò F, Salvatore M, Aiello M. A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images. Heliyon 2023; 9:e14371. [PMID: 36950640 PMCID: PMC10025040 DOI: 10.1016/j.heliyon.2023.e14371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023] Open
Abstract
Background and objectives The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. Methods A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results 21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. Conclusions The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.
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Affiliation(s)
| | - Valentina Brancato
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
- Corresponding author.
| | - Carlo Cavaliere
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Claudio 21, Naples, 80125, Italy
| | - Marco Salvatore
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
| | - Marco Aiello
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Naples, 80143, Italy
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9
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Pathological Detection of Micro and Fuzzy Gastric Cancer Cells Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023. [DOI: 10.1155/2023/5147399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
In recent years, with the increasing incidence of cancer, regular physical examination is an important way to find cancer. Nuclear screening is an important method for the diagnosis of gastrointestinal diseases, but it is challenging in the face of small and fuzzy gastrointestinal images. Different from traditional medical objects, pathological slice images are mostly blurry and tiny, which is somewhat difficult to detect and segment. The traditional diagnostic method lacks rapid quantitative analysis and has a certain delay in medical diagnosis, and traditional image processing uses morphological features and pixel distribution to extract features; it is often difficult to achieve the desired effect on small blurry images. This paper proposes a small, microfuzzy pathology detection algorithm based on the attention mechanism; the YOLOv5 is improved under small and micro fuzzy scenarios of the detection of cancer cells in the full field of digital pathology and tests it in the gastric cancer slice dataset. The network structure is improved, and the ability to learn features on small and micro targets is enhanced according to the law of feature distribution. Spatial and channel changes in network attention and attention weight distribution. In the deep blur scenario, the attention mechanism is added to optimize its recognition ability, and the test result shows F1_score is 0.616, and the mAP is 0.611, which can provide the decision support for clinical judgment.
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10
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Chan RC, To CKC, Cheng KCT, Yoshikazu T, Yan LLA, Tse GM. Artificial intelligence in breast cancer histopathology. Histopathology 2023; 82:198-210. [PMID: 36482271 DOI: 10.1111/his.14820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022]
Abstract
This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists-namely, molecular and hormonal analysis, grading, mitotic figure counting, ki-67 indexing, tumour-infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open-source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.
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Affiliation(s)
- Ronald Ck Chan
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Chun Kit Curtis To
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Ka Chuen Tom Cheng
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Tada Yoshikazu
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Lai Ling Amy Yan
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Gary M Tse
- Department of Anatomical and Cellular Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
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11
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Rashmi R, Prasad K, Udupa CBK. Region-based feature enhancement using channel-wise attention for classification of breast histopathological images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07966-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
AbstractBreast histopathological image analysis at 400x magnification is essential for the determination of malignant breast tumours. But manual analysis of these images is tedious, subjective, error-prone and requires domain knowledge. To this end, computer-aided tools are gaining much attention in the recent past as it aids pathologists and save time. Furthermore, advances in computational power have leveraged the usage of computer tools. Yet, usage of computer-aided tools to analyse these images is challenging due to various reasons such as heterogeneity of malignant tumours, colour variations and presence of artefacts. Moreover, these images are captured at high resolutions which pose a major challenge to designing deep learning models as it demands high computational requirements. In this context, the present work proposes a new approach to efficiently and effectively extract features from these high-resolution images. In addition, at 400x magnification, the characteristics and structure of nuclei play a prominent role in the decision of malignancy. In this regard, the study introduces a novel CNN architecture called as CWA-Net that uses a colour channel attention module to enhance the features of the potential regions of interest such as nuclei. The developed model is qualitatively and quantitatively evaluated on private and public datasets and achieved an accuracy of 0.95% and 0.96%, respectively. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art methods on both datasets.
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12
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Mezher MA, Altamimi A, Altamimi R. An enhanced Genetic Folding algorithm for prostate and breast cancer detection. PeerJ Comput Sci 2022; 8:e1015. [PMID: 35875638 PMCID: PMC9299265 DOI: 10.7717/peerj-cs.1015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 06/01/2022] [Indexed: 06/15/2023]
Abstract
Cancer's genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model's area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.
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Affiliation(s)
| | - Almothana Altamimi
- Department of Clinical Medicine and Surgery, Università degli Studi di Napoli Federico, di Napoli Federico, Italy
| | - Ruhaifa Altamimi
- Department of Business and Data Analytics, University of Huddersfield, Huddersfield, United Kingdom
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13
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Dadhania V, Gonzalez D, Yousif M, Cheng J, Morgan TM, Spratt DE, Reichert ZR, Mannan R, Wang X, Chinnaiyan A, Cao X, Dhanasekaran SM, Chinnaiyan AM, Pantanowitz L, Mehra R. Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer. BMC Cancer 2022; 22:494. [PMID: 35513774 PMCID: PMC9069768 DOI: 10.1186/s12885-022-09559-4] [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: 11/03/2021] [Accepted: 04/17/2022] [Indexed: 11/30/2022] Open
Abstract
Background TMPRSS2-ERG gene rearrangement, the most common E26 transformation specific (ETS) gene fusion within prostate cancer, is known to contribute to the pathogenesis of this disease and carries diagnostic annotations for prostate cancer patients clinically. The ERG rearrangement status in prostatic adenocarcinoma currently cannot be reliably identified from histologic features on H&E-stained slides alone and hence requires ancillary studies such as immunohistochemistry (IHC), fluorescent in situ hybridization (FISH) or next generation sequencing (NGS) for identification. Methods Objective We accordingly sought to develop a deep learning-based algorithm to identify ERG rearrangement status in prostatic adenocarcinoma based on digitized slides of H&E morphology alone. Design Setting, and Participants: Whole slide images from 392 in-house and TCGA cases were employed and annotated using QuPath. Image patches of 224 × 224 pixel were exported at 10 ×, 20 ×, and 40 × for input into a deep learning model based on MobileNetV2 convolutional neural network architecture pre-trained on ImageNet. A separate model was trained for each magnification. Training and test datasets consisted of 261 cases and 131 cases, respectively. The output of the model included a prediction of ERG-positive (ERG rearranged) or ERG-negative (ERG not rearranged) status for each input patch. Outcome measurements and statistical analysis: Various accuracy measurements including area under the curve (AUC) of the receiver operating characteristic (ROC) curves were used to evaluate the deep learning model. Results and Limitations All models showed similar ROC curves with AUC results ranging between 0.82 and 0.85. The sensitivity and specificity of these models were 75.0% and 83.1% (20 × model), respectively. Conclusions A deep learning-based model can successfully predict ERG rearrangement status in the majority of prostatic adenocarcinomas utilizing only H&E-stained digital slides. Such an artificial intelligence-based model can eliminate the need for using extra tumor tissue to perform ancillary studies in order to assess for ERG gene rearrangement in prostatic adenocarcinoma.
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Affiliation(s)
- Vipulkumar Dadhania
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Daniel Gonzalez
- Department of Pathology and Laboratory Medicine, Jackson Memorial Hospital, Miami, FL, USA
| | - Mustafa Yousif
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Pathology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jerome Cheng
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Todd M Morgan
- Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Daniel E Spratt
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Zachery R Reichert
- Department of Medical Oncology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Rahul Mannan
- Michigan Center for Translational Pathology, Ann Arbor, MI, USA
| | - Xiaoming Wang
- Michigan Center for Translational Pathology, Ann Arbor, MI, USA
| | - Anya Chinnaiyan
- Michigan Center for Translational Pathology, Ann Arbor, MI, USA
| | - Xuhong Cao
- Michigan Center for Translational Pathology, Ann Arbor, MI, USA
| | | | - Arul M Chinnaiyan
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.,Michigan Center for Translational Pathology, Ann Arbor, MI, USA.,Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA.,Howard Hughes Medical Institute, Ann Arbor, MI, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.,Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA
| | - Rohit Mehra
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA. .,Michigan Center for Translational Pathology, Ann Arbor, MI, USA. .,Rogel Cancer Center, Michigan Medicine, Ann Arbor, MI, USA.
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14
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Kashyap D, Pal D, Sharma R, Garg VK, Goel N, Koundal D, Zaguia A, Koundal S, Belay A. Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9605439. [PMID: 35480139 PMCID: PMC9038417 DOI: 10.1155/2022/9605439] [Citation(s) in RCA: 139] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/25/2022] [Accepted: 03/21/2022] [Indexed: 02/07/2023]
Abstract
Breast cancer is a global cause for concern owing to its high incidence around the world. The alarming increase in breast cancer cases emphasizes the management of disease at multiple levels. The management should start from the beginning that includes stringent cancer screening or cancer registry to effective diagnostic and treatment strategies. Breast cancer is highly heterogeneous at morphology as well as molecular levels and needs different therapeutic regimens based on the molecular subtype. Breast cancer patients with respective subtype have different clinical outcome prognoses. Breast cancer heterogeneity emphasizes the advanced molecular testing that will help on-time diagnosis and improved survival. Emerging fields such as liquid biopsy and artificial intelligence would help to under the complexity of breast cancer disease and decide the therapeutic regimen that helps in breast cancer management. In this review, we have discussed various risk factors and advanced technology available for breast cancer diagnosis to combat the worst breast cancer status and areas that need to be focused for the better management of breast cancer.
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Affiliation(s)
- Dharambir Kashyap
- Department of Histopathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Deeksha Pal
- Department of Translational and Regenerative Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Riya Sharma
- Department of Pulmonary Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Vivek Kumar Garg
- Department of Medical Laboratory Technology, University Institute of Applied Health Sciences, Chandigarh University (Gharuan), Mohali 140313, India
| | - Neelam Goel
- Department of Information Technology, University Institute of Engineering & Technology, Panjab University, Chandigarh 160014, India
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India
| | - Atef Zaguia
- Department of computer science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif 21944, Saudi Arabia
| | - Shubham Koundal
- Department of Medical Laboratory Technology, University Institute of Applied Health Sciences, Chandigarh University (Gharuan), Mohali 140313, India
| | - Assaye Belay
- Department of Statistics, Mizan-Tepi University, Ethiopia
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15
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Heinz CN, Echle A, Foersch S, Bychkov A, Kather JN. The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups. Histopathology 2022; 80:1121-1127. [PMID: 35373378 DOI: 10.1111/his.14659] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/17/2022] [Accepted: 04/02/2022] [Indexed: 11/30/2022]
Abstract
AIMS Artificial intelligence (AI) provides a powerful tool to extract information from digitized histopathology whole slide images. In the last five years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into. METHODS To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing sub-fields of computational pathology with a focus on solid tumors. RESULTS The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in sub-groups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high across subgroups. CONCLUSIONS Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.
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Affiliation(s)
- Céline N Heinz
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Amelie Echle
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Sebastian Foersch
- Department of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.,Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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16
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Ali H, Haq IU, Cui L, Feng J. MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network. BMC Med Inform Decis Mak 2022; 22:90. [PMID: 35379228 PMCID: PMC8978355 DOI: 10.1186/s12911-022-01826-5] [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: 09/06/2021] [Accepted: 03/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background The digital pathology images obtain the essential information about the patient’s disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of convolutional neural networks in image processing, deep learning has been shown to play a significant role in the various analysis of medical images, such as nuclei segmentation, mitosis detection and segmentation etc. Recently, several U-net based methods have been developed to solve the automated nuclei segmentation problems. However, these methods fail to deal with the weak features representation from the initial layers and introduce the noise into the decoder path. In this paper, we propose a multiscale attention learning network (MSAL-Net), where the dense dilated convolutions block captures more comprehensive nuclei context information, and a newly modified decoder part is introduced, which integrates with efficient channel attention and boundary refinement modules to effectively learn spatial information for better prediction and further refine the nuclei cell of boundaries. Results Both qualitative and quantitative results are obtained on the publicly available MoNuseg dataset. Extensive experiment results verify that our proposed method significantly outperforms state-of-the-art methods as well as the vanilla Unet method in the segmentation task. Furthermore, we visually demonstrate the effect of our modified decoder part. Conclusion The MSAL-Net shows superiority with a novel decoder to segment the touching and blurred background nuclei cells obtained from histopathology images with better performance for accurate decoding.
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Affiliation(s)
- Haider Ali
- School of Information Science and Technology, Northwest University, Xian, China
| | - Imran Ul Haq
- School of Information Science and Technology, Northwest University, Xian, China
| | - Lei Cui
- School of Information Science and Technology, Northwest University, Xian, China.
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xian, China.
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17
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Yoshizawa K, Ando H, Kimura Y, Kawashiri S, Yokomichi H, Moroi A, Ueki K. Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral squamous cell carcinoma using digital microscopic images: a retrospective study. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 133:441-452. [PMID: 35165068 DOI: 10.1016/j.oooo.2021.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/02/2021] [Accepted: 10/06/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE The Yamamoto-Kohama criteria are clinically useful for determining the mode of tumor invasion, especially in Japan. However, this evaluation method is based on subjective visual findings and has led to significant differences in determinations between evaluators and facilities. In this retrospective study, we aimed to develop an automatic method of determining the mode of invasion based on the processing of digital medical images. STUDY DESIGN Using 101 digitized photographic images of anonymized stained specimen slides, we created a classifier that allowed clinicians to introduce feature values and subjected the cases to machine learning using a random forest approach. We then compared the Yamamoto-Kohama grades (1, 2, 3, 4C, 4D) determined by a human oral and maxillofacial surgeon with those determined using the machine learning approach. RESULTS The input of multiple test images into the newly created classifier yielded an overall F-measure value of 87% (grade 1, 93%; grade 2, 67%; grade 3, 89%; grade 4C, 83%; grade 4D, 94%). These results suggest that the output of the classifier was very similar to the judgments of the clinician. CONCLUSIONS This system may be valuable for diagnostic support to provide an accurate determination of the mode of invasion.
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Affiliation(s)
- Kunio Yoshizawa
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan.
| | - Hidetoshi Ando
- Department of Media Engineering, Graduate School of University of Yamanashi, Kofu, Yamanashi, Japan
| | - Yujiro Kimura
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Shuichi Kawashiri
- Department of Oral and Maxillofacial Surgery, Kanazawa University Graduate School of Medical Science, Kanazawa, Ishikawa, Japan
| | - Hiroshi Yokomichi
- Department of Health Sciences, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Akinori Moroi
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Koichiro Ueki
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan
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18
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Chattopadhyay S, Dey A, Singh PK, Sarkar R. DRDA-Net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images. Comput Biol Med 2022; 145:105437. [PMID: 35339096 DOI: 10.1016/j.compbiomed.2022.105437] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/24/2022] [Accepted: 03/20/2022] [Indexed: 01/19/2023]
Abstract
Breast cancer is caused by the uncontrolled growth and division of cells in the breast, whereby a mass of tissue called a tumor is created. Early detection of breast cancer can save many lives. Hence, many researchers worldwide have invested considerable effort in developing robust computer-aided tools for the classification of breast cancer using histopathological images. For this purpose, in this study we designed a dual-shuffle attention-guided deep learning model, called the dense residual dual-shuffle attention network (DRDA-Net). Inspired by the bottleneck unit of the ShuffleNet architecture, in our proposed model we incorporate a channel attention mechanism, which enhances the model's ability to learn the complex patterns of images. Moreover, the model's densely connected blocks address both the overfitting and the vanishing gradient problem, although the model is trained on a substantially small dataset. We have evaluated our proposed model on the publicly available BreaKHis dataset and achieved classification accuracies of 95.72%, 94.41%, 97.43% and 98.1% on four different magnification levels i.e., 40x, 1000x, 200x, 400x respectively which proves the supremacy of the proposed model. The relevant code of the proposed DRDA-Net model can be foundt at: https://github.com/SohamChattopadhyayEE/DRDA-Net.
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Affiliation(s)
- Soham Chattopadhyay
- Department of Electrical Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata, 700032, West Bengal, India.
| | - Arijit Dey
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, 700064, West Bengal, India.
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata, 700106, West Bengal, India.
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata, 700032, West Bengal, India.
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19
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Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021; 46:7. [PMID: 34860316 PMCID: PMC8642363 DOI: 10.1007/s10916-021-01786-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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Affiliation(s)
- R Rashmi
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
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20
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Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Arch 2021; 480:191-209. [PMID: 34791536 DOI: 10.1007/s00428-021-03213-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/12/2021] [Accepted: 09/27/2021] [Indexed: 12/12/2022]
Abstract
The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on "deep learning" neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.
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Affiliation(s)
- Mustafa Yousif
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Arvydas Laurinavicius
- Department of Pathology, Pharmacology and Forensic Medicine, Faculty of Medicine, Vilnius University, and National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - David Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, and Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
| | - Stuart Schnitt
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
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21
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R R, Prasad K, Udupa CBK. BCHisto-Net: Breast histopathological image classification by global and local feature aggregation. Artif Intell Med 2021; 121:102191. [PMID: 34763806 DOI: 10.1016/j.artmed.2021.102191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/15/2021] [Accepted: 10/05/2021] [Indexed: 02/06/2023]
Abstract
Breast cancer among women is the second most common cancer worldwide. Non-invasive techniques such as mammograms and ultrasound imaging are used to detect the tumor. However, breast histopathological image analysis is inevitable for the detection of malignancy of the tumor. Manual analysis of breast histopathological images is subjective, tedious, laborious and is prone to human errors. Recent developments in computational power and memory have made automation a popular choice for the analysis of these images. One of the key challenges of breast histopathological image classification at 100× magnification is to extract the features of the potential regions of interest to decide on the malignancy of the tumor. The current state-of-the-art CNN based methods for breast histopathological image classification extract features from the entire image (global features) and thus may overlook the features of the potential regions of interest. This can lead to inaccurate diagnosis of breast histopathological images. This research gap has motivated us to propose BCHisto-Net to classify breast histopathological images at 100× magnification. The proposed BCHisto-Net extracts both global and local features required for the accurate classification of breast histopathological images. The global features extract abstract image features while local features focus on potential regions of interest. Furthermore, a feature aggregation branch is proposed to combine these features for the classification of 100× images. The proposed method is quantitatively evaluated on red a private dataset and publicly available BreakHis dataset. An extensive evaluation of the proposed model showed the effectiveness of the local and global features for the classification of these images. The proposed method achieved an accuracy of 95% and 89% on KMC and BreakHis datasets respectively, outperforming state-of-the-art classifiers.
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Affiliation(s)
- Rashmi R
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India.
| | - Chethana Babu K Udupa
- Department of Pathology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
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22
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Vuong TTL, Song B, Kim K, Cho YM, Kwak JT. Multi-scale binary pattern encoding network for cancer classification in pathology images. IEEE J Biomed Health Inform 2021; 26:1152-1163. [PMID: 34310334 DOI: 10.1109/jbhi.2021.3099817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Multi-scale approaches have been widely studied in pathology image analysis. These offer an ability to characterize tissues in an image at various scales, in which the tissues may appear differently. Many of such methods have focused on extracting multi-scale hand-crafted features and applied them to various tasks in pathology image analysis. Even, several deep learning methods explicitly adopt the multi-scale approaches. However, most of these methods simply merge the multi-scale features together or adopt the coarse-to-fine/fine-to-coarse strategy, which uses the features one at a time in a sequential manner. Utilizing the multi-scale features in a cooperative and discriminative fashion, the learning capabilities could be further improved. Herein, we propose a multi-scale approach that can identify and leverage the patterns of the multiple scales within a deep neural network and provide the superior capability of cancer classification. The patterns of the features across multiple scales are encoded as a binary pattern code and further converted to a decimal number, which can be easily embedded in the current framework of the deep neural networks. To evaluate the proposed method, multiple sets of pathology images are employed. Under the various experimental settings, the proposed method is systematically assessed and shows an improved classification performance in comparison to other competing methods.
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23
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A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer. EBioMedicine 2021; 70:103492. [PMID: 34280779 PMCID: PMC8318866 DOI: 10.1016/j.ebiom.2021.103492] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/30/2021] [Accepted: 06/30/2021] [Indexed: 12/21/2022] Open
Abstract
Background Tumor-infiltrating lymphocytes (TILs) are clinically significant in triple-negative breast cancer (TNBC). Although a standardized methodology for visual TILs assessment (VTA) exists, it has several inherent limitations. We established a deep learning-based computational TIL assessment (CTA) method broadly following VTA guideline and compared it with VTA for TNBC to determine the prognostic value of the CTA and a reasonable CTA workflow for clinical practice. Methods We trained three deep neural networks for nuclei segmentation, nuclei classification and necrosis classification to establish a CTA workflow. The automatic TIL (aTIL) score generated was compared with manual TIL (mTIL) scores provided by three pathologists in an Asian (n = 184) and a Caucasian (n = 117) TNBC cohort to evaluate scoring concordance and prognostic value. Findings The intraclass correlations (ICCs) between aTILs and mTILs varied from 0.40 to 0.70 in two cohorts. Multivariate Cox proportional hazards analysis revealed that the aTIL score was associated with disease free survival (DFS) in both cohorts, as either a continuous [hazard ratio (HR)=0.96, 95% CI 0.94–0.99] or dichotomous variable (HR=0.29, 95% CI 0.12–0.72). A higher C-index was observed in a composite mTIL/aTIL three-tier stratification model than in the dichotomous model, using either mTILs or aTILs alone. Interpretation The current study provides a useful tool for stromal TIL assessment and prognosis evaluation for patients with TNBC. A workflow integrating both VTA and CTA may aid pathologists in performing risk management and decision-making tasks.
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24
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Corredor G, Toro P, Bera K, Rasmussen D, Viswanathan VS, Buzzy C, Fu P, Barton LM, Stroberg E, Duval E, Gilmore H, Mukhopadhyay S, Madabhushi A. Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings. J Med Imaging (Bellingham) 2021; 8:017501. [PMID: 34268443 DOI: 10.1117/1.jmi.8.s1.017501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 06/28/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose: We used computerized image analysis and machine learning approaches to characterize spatial arrangement features of the immune response from digitized autopsied H&E tissue images of the lung in coronavirus disease 2019 (COVID-19) patients. Additionally, we applied our approach to tease out potential morphometric differences from autopsies of patients who succumbed to COVID-19 versus H1N1. Approach: H&E lung whole slide images from autopsy specimens of nine COVID-19 and two H1N1 patients were computationally interrogated. 606 image patches ( ∼ 55 per patient) of 1024 × 882 pixels were extracted from the 11 autopsied patient studies. A watershed-based segmentation approach in conjunction with a machine learning classifier was employed to identify two types of nuclei families: lymphocytes and non-lymphocytes (i.e., other nucleated cells such as pneumocytes, macrophages, and neutrophils). Based off the proximity of the individual nuclei, clusters for each nuclei family were constructed. For each of the resulting clusters, a series of quantitative measurements relating to architecture and density of nuclei clusters were calculated. A receiver operating characteristics-based feature selection method, violin plots, and the t-distributed stochastic neighbor embedding algorithm were employed to study differences in immune patterns. Results: In COVID-19, the immune response consistently showed multiple small-size lymphocyte clusters, suggesting that lymphocyte response is rather modest, possibly due to lymphocytopenia. In H1N1, we found larger lymphocyte clusters that were proximal to large clusters of non-lymphocytes, a possible reflection of increased prevalence of macrophages and other immune cells. Conclusion: Our study shows the potential of computational pathology to uncover immune response features that may not be obvious by routine histopathology visual inspection.
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Affiliation(s)
- Germán Corredor
- Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, Cleveland, Ohio, United States.,Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, United States
| | - Paula Toro
- Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, Cleveland, Ohio, United States
| | - Kaustav Bera
- Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, Cleveland, Ohio, United States
| | - Dylan Rasmussen
- Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, Cleveland, Ohio, United States
| | - Vidya Sankar Viswanathan
- Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, Cleveland, Ohio, United States
| | - Christina Buzzy
- Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, Cleveland, Ohio, United States
| | - Pingfu Fu
- Case Western Reserve University, Department of Population and Quantitative Health Sciences, Cleveland, Ohio, United States
| | - Lisa M Barton
- Oklahoma Office of the Chief Medical Examiner, Oklahoma City, Oklahoma, United States
| | - Edana Stroberg
- Oklahoma Office of the Chief Medical Examiner, Oklahoma City, Oklahoma, United States
| | - Eric Duval
- Oklahoma Office of the Chief Medical Examiner, Oklahoma City, Oklahoma, United States
| | - Hannah Gilmore
- University Hospitals, Department of Pathology, Cleveland, Ohio, United States
| | | | - Anant Madabhushi
- Case Western Reserve University, Center for Computational Imaging and Personalized Diagnostics, Cleveland, Ohio, United States.,Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, United States
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25
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Thagaard J, Stovgaard ES, Vognsen LG, Hauberg S, Dahl A, Ebstrup T, Doré J, Vincentz RE, Jepsen RK, Roslind A, Kümler I, Nielsen D, Balslev E. Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers. Cancers (Basel) 2021; 13:3050. [PMID: 34207414 PMCID: PMC8235502 DOI: 10.3390/cancers13123050] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 12/18/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72-0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.
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Affiliation(s)
- Jeppe Thagaard
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; (L.G.V.); (S.H.); (A.D.)
- Visiopharm A/S, 2970 Hørsholm, Denmark; (T.E.); (J.D.)
| | - Elisabeth Specht Stovgaard
- Department of Pathology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark; (E.S.S.); (R.E.V.); (R.K.J.); (A.R.); (E.B.)
| | - Line Grove Vognsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; (L.G.V.); (S.H.); (A.D.)
- Visiopharm A/S, 2970 Hørsholm, Denmark; (T.E.); (J.D.)
| | - Søren Hauberg
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; (L.G.V.); (S.H.); (A.D.)
| | - Anders Dahl
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark; (L.G.V.); (S.H.); (A.D.)
| | | | - Johan Doré
- Visiopharm A/S, 2970 Hørsholm, Denmark; (T.E.); (J.D.)
| | - Rikke Egede Vincentz
- Department of Pathology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark; (E.S.S.); (R.E.V.); (R.K.J.); (A.R.); (E.B.)
| | - Rikke Karlin Jepsen
- Department of Pathology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark; (E.S.S.); (R.E.V.); (R.K.J.); (A.R.); (E.B.)
| | - Anne Roslind
- Department of Pathology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark; (E.S.S.); (R.E.V.); (R.K.J.); (A.R.); (E.B.)
| | - Iben Kümler
- Department of Oncology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark; (I.K.); (D.N.)
| | - Dorte Nielsen
- Department of Oncology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark; (I.K.); (D.N.)
| | - Eva Balslev
- Department of Pathology, Herlev and Gentofte Hospital, 2730 Herlev, Denmark; (E.S.S.); (R.E.V.); (R.K.J.); (A.R.); (E.B.)
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26
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Improved bag-of-features using grey relational analysis for classification of histology images. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00275-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractAn efficient classification method to categorize histopathological images is a challenging research problem. In this paper, an improved bag-of-features approach is presented as an efficient image classification method. In bag-of-features, a large number of keypoints are extracted from histopathological images that increases the computational cost of the codebook construction step. Therefore, to select the a relevant subset of keypoints, a new keypoints selection method is introduced in the bag-of-features method. To validate the performance of the proposed method, an extensive experimental analysis is conducted on two standard histopathological image datasets, namely ADL and Blue histology datasets. The proposed keypoint selection method reduces the extracted high dimensional features by 95% and 68% from the ADL and Blue histology datasets respectively with less computational time. Moreover, the enhanced bag-of-features method increases classification accuracy by from other considered classification methods.
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27
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Gavrielides MA, Miller M, Hagemann IS, Abdelal H, Alipour Z, Chen JF, Salari B, Sun L, Zhou H, Seidman JD. Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees. Arch Pathol Lab Med 2021; 144:869-877. [PMID: 31816269 DOI: 10.5858/arpa.2019-0390-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2019] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. OBJECTIVE.— To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. DESIGN.— Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. RESULTS.— Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. CONCLUSIONS.— Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks.
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Affiliation(s)
- Marios A Gavrielides
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Meghan Miller
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Ian S Hagemann
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Heba Abdelal
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Zahra Alipour
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Jie-Fu Chen
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Behzad Salari
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Lulu Sun
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Huifang Zhou
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
| | - Jeffrey D Seidman
- From the Division of Imaging, Diagnostics, and Software Reliability, Office of Engineering and Science Laboratories (Dr Gavrielides and Ms Miller), and the Office of In Vitro Diagnostics and Radiological Health, Division of Molecular Genetics and Pathology (Dr Seidman), Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland; the Department of Bioengineering, University of Maryland, College Park (Ms Miller); and the Departments of Pathology and Immunology (Drs Hagemann, Abdelal, Alipour, Chen, Salari, Sun, and Zhou) and Obstetrics and Gynecology (Dr Hagemann), Washington University School of Medicine, St Louis, Missouri. Ms Miller is currently with PCTEST Engineering Laboratory, Columbia, Maryland
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Nasir IM, Rashid M, Shah JH, Sharif M, Awan MYH, Alkinani MH. An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set. Curr Med Imaging 2021; 17:136-147. [PMID: 32324518 DOI: 10.2174/1573405616666200423085826] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 03/24/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. OBJECTIVE Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system. METHODS A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pre-trained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of the proposed method. RESULTS The method concentrates on histopathological images to classify the breast cancer. The performance is compared with the state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. CONCLUSION The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.
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Affiliation(s)
| | - Muhammad Rashid
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Jamal Hussain Shah
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | | | - Monagi H Alkinani
- College of Computer Science and Engineering, Department of Computer Science and Artificial Intelligence, University of Jeddah, Saudi Arabia
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Rajathi GM. Optimized Radial Basis Neural Network for Classification of Breast Cancer Images. Curr Med Imaging 2021; 17:97-108. [PMID: 32416697 DOI: 10.2174/1573405616666200516172118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/18/2020] [Accepted: 04/25/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer is a curable disease if diagnosed at an early stage. The chances of having breast cancer are the lowest in married women after the breast-feeding phase because the cancer is formed from the blocked milk ducts. INTRODUCTION Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence- based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by the image analysing, detection, screening, diagnosing, and other performance measures. METHODS The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose. RESULTS Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature. CONCLUSION Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter, and the classifier extracts the feature based on the breast image.
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Affiliation(s)
- G M Rajathi
- Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
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Wang M, Aung PP, Prieto VG. Standardized Method for Defining a 1-mm2 Region of Interest for Calculation of Mitotic Rate on Melanoma Whole Slide Images. Arch Pathol Lab Med 2021; 145:1255-1263. [PMID: 33417687 DOI: 10.5858/arpa.2020-0137-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Mitotic rate counting is essential in pathologic evaluations in melanoma. The American Joint Committee on Cancer recommends reporting the number of mitotic figures (MFs) in a 1-mm2 area encompassing the "hot spot." There is currently no standard procedure for delineating a 1-mm2 region of interest for MF counting on a digital whole slide image (WSI) of melanoma. OBJECTIVE.— To establish a standardized method to enclose a 1-mm2 region of interest for MF counting in melanoma based on WSIs and assess the method's effectiveness. DESIGN.— Whole slide images were visualized using the ImageScope viewer (Aperio). Different monitors and viewing magnifications were explored and the annotation tools provided by ImageScope were evaluated. For validation, we compared mitotic rates obtained from WSIs with our method and those from glass slides with traditional microscopy with 30 melanoma cases. RESULTS.— Of the monitors we examined, a 32-inch monitor with 3840 × 2160 resolution was optimal for counting MFs within a 1-mm2 region of interest in melanoma. When WSIs were viewed in the ImageScope viewer, ×10 to ×20 magnification during screening could efficiently locate a hot spot and ×20 to ×40 magnification during counting could accurately identify MFs. Fixed-shape annotations with 500 × 500-μm squares or circles can precisely and efficiently enclose a 1-mm2 region of interest. Our method on WSIs was able to produce a higher mitotic rate than with glass slides. CONCLUSIONS.— Whole slide images may be used to efficiently count MFs. We recommend fixed-shape annotation with 500 × 500-μm squares or circles for routine practice in counting MFs for melanoma.
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Affiliation(s)
- Minhua Wang
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Phyu P Aung
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Victor G Prieto
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
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Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends. MATHEMATICS 2020. [DOI: 10.3390/math8111863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.
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Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion. Comput Biol Med 2020; 124:103954. [DOI: 10.1016/j.compbiomed.2020.103954] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 07/30/2020] [Accepted: 07/30/2020] [Indexed: 01/22/2023]
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Wan T, Zhao L, Feng H, Li D, Tong C, Qin Z. Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.08.103] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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La Barbera D, Polónia A, Roitero K, Conde-Sousa E, Della Mea V. Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning. J Imaging 2020; 6:82. [PMID: 34460739 PMCID: PMC8321042 DOI: 10.3390/jimaging6090082] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/14/2020] [Accepted: 08/18/2020] [Indexed: 12/28/2022] Open
Abstract
Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin-Eosin slides, which partly mimics the pathologist's behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score.
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Affiliation(s)
- David La Barbera
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy; (D.L.B.); (K.R.)
| | - António Polónia
- Department of Pathology, Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto, 4169-007 Porto, Portugal;
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4169-007 Porto, Portugal;
| | - Kevin Roitero
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy; (D.L.B.); (K.R.)
| | - Eduardo Conde-Sousa
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4169-007 Porto, Portugal;
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4169-007 Porto, Portugal
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy; (D.L.B.); (K.R.)
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Amgad M, Stovgaard ES, Balslev E, Thagaard J, Chen W, Dudgeon S, Sharma A, Kerner JK, Denkert C, Yuan Y, AbdulJabbar K, Wienert S, Savas P, Voorwerk L, Beck AH, Madabhushi A, Hartman J, Sebastian MM, Horlings HM, Hudeček J, Ciompi F, Moore DA, Singh R, Roblin E, Balancin ML, Mathieu MC, Lennerz JK, Kirtani P, Chen IC, Braybrooke JP, Pruneri G, Demaria S, Adams S, Schnitt SJ, Lakhani SR, Rojo F, Comerma L, Badve SS, Khojasteh M, Symmans WF, Sotiriou C, Gonzalez-Ericsson P, Pogue-Geile KL, Kim RS, Rimm DL, Viale G, Hewitt SM, Bartlett JMS, Penault-Llorca F, Goel S, Lien HC, Loibl S, Kos Z, Loi S, Hanna MG, Michiels S, Kok M, Nielsen TO, Lazar AJ, Bago-Horvath Z, Kooreman LFS, van der Laak JAWM, Saltz J, Gallas BD, Kurkure U, Barnes M, Salgado R, Cooper LAD. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer 2020; 6:16. [PMID: 32411818 PMCID: PMC7217824 DOI: 10.1038/s41523-020-0154-2] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 02/18/2020] [Indexed: 02/07/2023] Open
Abstract
Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.
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Affiliation(s)
- Mohamed Amgad
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
| | | | - Eva Balslev
- Department of Pathology, Herlev and Gentofte Hospital, University of Copenhagen, Herlev, Denmark
| | - Jeppe Thagaard
- DTU Compute, Department of Applied Mathematics, Technical University of Denmark, Lyngby, Denmark
- Visiopharm A/S, Hørsholm, Denmark
| | - Weijie Chen
- FDA/CDRH/OSEL/Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD USA
| | - Sarah Dudgeon
- FDA/CDRH/OSEL/Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
| | | | - Carsten Denkert
- Institut für Pathologie, Universitätsklinikum Gießen und Marburg GmbH, Standort Marburg, Philipps-Universität Marburg, Marburg, Germany
- Institute of Pathology, Philipps-University Marburg, Marburg, Germany
- German Cancer Consortium (DKTK), Partner Site Charité, Berlin, Germany
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Khalid AbdulJabbar
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Stephan Wienert
- Institut für Pathologie, Universitätsklinikum Gießen und Marburg GmbH, Standort Marburg, Philipps-Universität Marburg, Marburg, Germany
| | - Peter Savas
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
| | - Leonie Voorwerk
- Department of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH USA
| | - Johan Hartman
- Department of Oncology and Pathology, Karolinska Institutet and University Hospital, Solna, Sweden
| | - Manu M. Sebastian
- Departments of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Hugo M. Horlings
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan Hudeček
- Department of Research IT, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - David A. Moore
- Department of Pathology, UCL Cancer Institute, London, UK
| | - Rajendra Singh
- Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Elvire Roblin
- Université Paris-Saclay, Univ. Paris-Sud, Villejuif, France
| | - Marcelo Luiz Balancin
- Department of Pathology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Marie-Christine Mathieu
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Jochen K. Lennerz
- Department of Pathology, Massachusetts General Hospital, Boston, MA USA
| | - Pawan Kirtani
- Department of Histopathology, Manipal Hospitals Dwarka, New Delhi, India
| | - I-Chun Chen
- Department of Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Jeremy P. Braybrooke
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Medical Oncology, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Giancarlo Pruneri
- Pathology Department, Fondazione IRCCS Istituto Nazionale Tumori and University of Milan, School of Medicine, Milan, Italy
| | | | - Sylvia Adams
- Laura and Isaac Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY USA
| | - Stuart J. Schnitt
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA USA
| | - Sunil R. Lakhani
- The University of Queensland Centre for Clinical Research and Pathology Queensland, Brisbane, Australia
| | - Federico Rojo
- Pathology Department, CIBERONC-Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Madrid, Spain
- GEICAM-Spanish Breast Cancer Research Group, Madrid, Spain
| | - Laura Comerma
- Pathology Department, CIBERONC-Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Madrid, Spain
- GEICAM-Spanish Breast Cancer Research Group, Madrid, Spain
| | - Sunil S. Badve
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN USA
| | | | - W. Fraser Symmans
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles (ULB), Brussels, Belgium
- ULB-Cancer Research Center (U-CRC) Université Libre de Bruxelles, Brussels, Belgium
| | - Paula Gonzalez-Ericsson
- Breast Cancer Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | | | | | - David L. Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT USA
| | - Giuseppe Viale
- Department of Pathology, IEO, European Institute of Oncology IRCCS & State University of Milan, Milan, Italy
| | - Stephen M. Hewitt
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - John M. S. Bartlett
- Ontario Institute for Cancer Research, Toronto, ON Canada
- Edinburgh Cancer Research Centre, Western General Hospital, Edinburgh, UK
| | - Frédérique Penault-Llorca
- Department of Pathology and Molecular Pathology, Centre Jean Perrin, Clermont-Ferrand, France
- UMR INSERM 1240, Universite Clermont Auvergne, Clermont-Ferrand, France
| | - Shom Goel
- Victorian Comprehensive Cancer Centre building, Peter MacCallum Cancer Centre, Melbourne, Victoria Australia
| | - Huang-Chun Lien
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | - Sibylle Loibl
- German Breast Group, c/o GBG-Forschungs GmbH, Neu-Isenburg, Germany
| | - Zuzana Kos
- Department of Pathology, BC Cancer, Vancouver, British Columbia Canada
| | - Sherene Loi
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Stefan Michiels
- Gustave Roussy, Universite Paris-Saclay, Villejuif, France
- Université Paris-Sud, Institut National de la Santé et de la Recherche Médicale, Villejuif, France
| | - Marleen Kok
- Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Alexander J. Lazar
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
- Department of Dermatology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | | | - Loes F. S. Kooreman
- GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Pathology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jeroen A. W. M. van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY USA
| | - Brandon D. Gallas
- FDA/CDRH/OSEL/Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD USA
| | - Uday Kurkure
- Roche Tissue Diagnostics, Digital Pathology, Santa Clara, CA USA
| | - Michael Barnes
- Roche Diagnostics Information Solutions, Belmont, CA USA
| | - Roberto Salgado
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Victoria, Australia
- Department of Pathology, GZA-ZNA Ziekenhuizen, Antwerp, Belgium
| | - Lee A. D. Cooper
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
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Ganesan S, Mehnert J. Biomarkers for Response to Immune Checkpoint Blockade. ANNUAL REVIEW OF CANCER BIOLOGY 2020. [DOI: 10.1146/annurev-cancerbio-030419-033604] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Immune checkpoint blockade (ICB) has significant clinical activity in diverse cancer classes and can induce durable remissions in even refractory advanced disease. However, only a minority of cancer patients treated with ICB have long-term benefits, and ICB treatment is associated with significant, potentially life-threatening, autoimmune side effects. There is a great need to develop biomarkers of response to guide patient selection to maximize the chance of benefit and prevent unnecessary toxicity, and current biomarkers do not have optimal positive or negative predictive value. A variety of potential biomarkers are currently being developed, including those based on assessment of checkpoint protein expression, evaluation of tumor-intrinsic features including mutation burden and viral infection, evaluation of features of the tumor immune microenvironment including nature of immune cell infiltration, and features of the host such as composition of the gut microbiome. Better understanding of the underlying fundamental mechanisms of immune response and resistance to ICB, along with the use of complementary assays that interrogate distinct features of the tumor, the tumor microenvironment, and host immune system, will allow more precise use of these therapies to optimize patient outcomes.
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Affiliation(s)
- Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA
| | - Janice Mehnert
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA
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Barsoum I, Tawedrous E, Faragalla H, Yousef GM. Histo-genomics: digital pathology at the forefront of precision medicine. ACTA ACUST UNITED AC 2020; 6:203-212. [PMID: 30827078 DOI: 10.1515/dx-2018-0064] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 09/28/2018] [Indexed: 12/26/2022]
Abstract
The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.
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Affiliation(s)
- Ivraym Barsoum
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Eriny Tawedrous
- Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Hala Faragalla
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - George M Yousef
- Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.,Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada
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38
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Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101671] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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39
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Ibrahim A, Gamble P, Jaroensri R, Abdelsamea MM, Mermel CH, Chen PHC, Rakha EA. Artificial intelligence in digital breast pathology: Techniques and applications. Breast 2019; 49:267-273. [PMID: 31935669 PMCID: PMC7375550 DOI: 10.1016/j.breast.2019.12.007] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/12/2019] [Indexed: 12/16/2022] Open
Abstract
Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.
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Affiliation(s)
- Asmaa Ibrahim
- Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK
| | | | | | - Mohammed M Abdelsamea
- School of Computing and Digital Technology, Birmingham City University, Birmingham, UK
| | | | | | - Emad A Rakha
- Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK.
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40
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Gurav SB, Kulhalli KV, Desai VV. PROSTATE CANCER DETECTION USING HISTOPATHOLOGY IMAGES AND CLASSIFICATION USING IMPROVED RideNN. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2019. [DOI: 10.4015/s101623721950042x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Medical industry reports prostate cancer as common and high among men and alarms the necessity for detecting prostate cancer for which the required morphology is extracted from the histopathology images. Commonly, the Gleason grading system remains a perfect factor for grading prostate cancer in men, but pathologists suffer from minute inter- and intra-observer variations. Thus, an automatic method for segmenting and classifying prostate cancer is modeled in this paper. The significance of the developed method is that the segmentation and classification are gland-oriented using the Color Space (CS) transformation and Salp Swarm Optimization Algorithm-based Rider Neural Network (SSA-RideNN). The gland region is considered as the morphology for cancer detection from which the maximal significant regions are extracted as features using multiple-kernel scale-invariant feature transform (MK-SIFT). Here, the RideNN classifier is trained optimally using the proposed Salp–Rider Algorithm (SRA), which is the integration of Salp Swarm Optimization Algorithm (SSA) and Rider Optimization Algorithm (ROA). The experimentation is performed using the histopathology images and the analysis based on sensitivity, accuracy, and specificity reveals that the proposed prostate cancer detection method acquired the maximal accuracy, sensitivity, and specificity of 0.8966, 0.8919, and 0.8596, respectively.
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Affiliation(s)
- Shashidhar B. Gurav
- Sharad Institute of Technology, College of Engineering, Ichal Karanji, Kolhapur 416121, Maharashtra, India
| | - Kshama V. Kulhalli
- D Y Patil College of Engineering and Technology, Kasaba Bawada, Kolhapur 416006, Maharashtra, India
| | - Veena V. Desai
- Department of Computer Science and Engineering, KLS Gogte Institute of Technology, Udyambag, Belagavi 590008, Karnataka, India
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Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 2019; 16:703-715. [PMID: 31399699 PMCID: PMC6880861 DOI: 10.1038/s41571-019-0252-y] [Citation(s) in RCA: 630] [Impact Index Per Article: 126.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2019] [Indexed: 02/06/2023]
Abstract
In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and 'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.
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Affiliation(s)
- Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kurt A Schalper
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Vamsidhar Velcheti
- Thoracic Medical Oncology, Perlmutter Cancer Center, New York University, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.
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Turner OC, Aeffner F, Bangari DS, High W, Knight B, Forest T, Cossic B, Himmel LE, Rudmann DG, Bawa B, Muthuswamy A, Aina OH, Edmondson EF, Saravanan C, Brown DL, Sing T, Sebastian MM. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicol Pathol 2019; 48:277-294. [DOI: 10.1177/0192623319881401] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text]
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Affiliation(s)
- Oliver C. Turner
- Novartis, Novartis Institutes for Biomedical Research, Preclinical Safety, East Hanover, NJ, USA
| | - Famke Aeffner
- Amgen Inc, Research, Comparative Biology and Safety Sciences, San Francisco, CA, USA
| | | | - Wanda High
- High Preclinical Pathology Consulting, Rochester, NY, USA
| | - Brian Knight
- Boehringer Ingelheim Pharmaceuticals Incorporated, Nonclinical Drug Safety, Ridgefield, CT, USA
| | | | - Brieuc Cossic
- Roche, Pharmaceutical Research and Early Development (pRED), Roche Innovation Center, Basel, Switzerland
| | - Lauren E. Himmel
- Division of Animal Care, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | | | - Elijah F. Edmondson
- Pathology/Histotechnology Laboratory, Frederick National Laboratory for Cancer Research, NIH, Frederick, MD, USA
| | - Chandrassegar Saravanan
- Novartis, Novartis Institutes for Biomedical Research, Preclinical Safety, Cambridge, MA, USA
| | | | - Tobias Sing
- Novartis, Novartis Institutes for Biomedical Research, NIBR Informatics, Basel, Switzerland
| | - Manu M. Sebastian
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, USA
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43
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Tumor Malignancy Detection Using Histopathology Imaging. J Med Imaging Radiat Sci 2019; 50:514-528. [PMID: 31501064 DOI: 10.1016/j.jmir.2019.07.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/27/2019] [Accepted: 07/08/2019] [Indexed: 11/20/2022]
Abstract
Image segmentation and classification in the biomedical imaging field has high worth in cancer diagnosis and grading. The proposed method classifies the images based on a combination of handcrafted features and shape features using bag of visual words (BoW). The multistage segmentation technique to localize the nuclei in histopathology images includes the stain decomposition and histogram equalization to highlight the nucleus region, followed by the nuclei key point extraction using fast radial symmetry transform, normalized graph cut based on the nuclei region estimation, and nuclei boundary estimation using modified gradient. Subsequently, features from localized regions termed as handcrafted features and the shape features using BoW are extracted for classification. The experiments are performed using both the handcrafted features and BoW to take the advantages of both local nuclei features and globally spatial features. The simulation is performed on the Bisque and BreakHis data sets (with corresponding average accuracies of 93.87% and 96.96%, respectively) and confirms better diagnosis performance using the proposed method.
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44
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Li X, Monga V, Rao UKA. Analysis-Synthesis Learning With Shared Features: Algorithms for Histology Image Classification. IEEE Trans Biomed Eng 2019; 67:1061-1073. [PMID: 31329103 DOI: 10.1109/tbme.2019.2928997] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE The diversity of tissue structure in histopathological images makes feature extraction for classification a challenging task. Dictionary learning within a sparse representation-based classification (SRC) framework has been shown to be successful for feature discovery. However, there exist stiff practical challenges: 1) computational complexity of SRC can be onerous in the decision stage since it involves solving a sparsity constrained optimization problem and often over a large number of image patches; and 2) images from distinct classes continue to share several geometric features. We propose a novel analysis-synthesis model learning with shared features algorithm (ALSF) for classifying such images more effectively. METHODS In the ALSF, a joint analysis and synthesis learning model is introduced to learn the classifier and the feature extractor at the same time. Unlike SRC, no explicit optimization is needed in the inference phase leading to much reduced computation. Crucially, we introduce the learning of a low-rank shared dictionary and a shared analysis operator, which more accurately represents both similarities and differences in histopathological images from distinct classes. We also develop an extension of ALSF with a sparsity constraint, whose presence or absence facilitates a cost-performance tradeoff. RESULTS The ALSF is evaluated on three challenging and well-known datasets: 1) spleen tissue images; 2) brain tumor images; and 3) breast cancer tissue dataset, provided by different organizations. CONCLUSION Experimental results demonstrate both complexity and performance benefits of the ALSF over state-of-the-art alternatives. SIGNIFICANCE Modeling shared features with appropriate quantitative constraints lead to significantly improved classification in histopathology.
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Turkki R, Byckhov D, Lundin M, Isola J, Nordling S, Kovanen PE, Verrill C, von Smitten K, Joensuu H, Lundin J, Linder N. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat 2019; 177:41-52. [PMID: 31119567 PMCID: PMC6647903 DOI: 10.1007/s10549-019-05281-1] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 05/16/2019] [Indexed: 12/12/2022]
Abstract
PURPOSE Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input. METHODS Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N = 1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients. RESULTS In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p = 0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p = 0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples. CONCLUSIONS Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.
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Affiliation(s)
- Riku Turkki
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. .,Science for Life Laboratory (SciLifeLab), Karolinska Institutet, Solna, Sweden.
| | - Dmitrii Byckhov
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Mikael Lundin
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jorma Isola
- Department of Cancer Biology, BioMediTech, University of Tampere, Tampere, Finland
| | - Stig Nordling
- Department of Pathology, Medicum, University of Helsinki, Helsinki, Finland
| | - Panu E Kovanen
- HUSLAB and Medicum, Helsinki University Hospital Cancer Center and University of Helsinki, Helsinki, Finland
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford, UK
| | | | - Heikki Joensuu
- Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Johan Lundin
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Nina Linder
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Women's and Children's Health, International Maternal and Child health (IMCH), Uppsala University, Uppsala, Sweden
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Ren J, Hacihaliloglu I, Singer EA, Foran DJ, Qi X. Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images. Front Bioeng Biotechnol 2019; 7:102. [PMID: 31158269 PMCID: PMC6529804 DOI: 10.3389/fbioe.2019.00102] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 04/23/2019] [Indexed: 11/13/2022] Open
Abstract
Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously providing insight as to the underlying mechanisms of disease onset and progression. A major challenge that is confronted when analyzing samples that have been prepared at disparate laboratories and institutions is that the algorithms used to assess the digitized specimens often exhibit heterogeneous staining characteristics because of slight differences in incubation times and the protocols used to prepare the samples. Unfortunately, such variations can render a prediction model learned from one batch of specimens ineffective for characterizing an ensemble originating from another site. In this work, we propose to adopt unsupervised domain adaptation to effectively transfer the discriminative knowledge obtained from any given source domain to the target domain without requiring any additional labeling or annotation of images at the target site. In this paper, our team investigates the use of two approaches for performing the adaptation: (1) color normalization and (2) adversarial training. The adversarial training strategy is implemented through the use of convolutional neural networks to find an invariant feature space and Siamese architecture within the target domain to add a regularization that is appropriate for the entire set of whole-slide images. The adversarial adaptation results in significant classification improvement compared with the baseline models under a wide range of experimental settings.
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Affiliation(s)
- Jian Ren
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, United States
| | - Ilker Hacihaliloglu
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, United States
| | - Eric A. Singer
- Section of Urologic Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
- Center for Biomedical Imaging and Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - David J. Foran
- Center for Biomedical Imaging and Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Xin Qi
- Center for Biomedical Imaging and Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
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Sari CT, Gunduz-Demir C. Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1139-1149. [PMID: 30403624 DOI: 10.1109/tmi.2018.2879369] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly relies on the features that they use, and thus, their success strictly depends on the ability of these features by successfully quantifying the histopathology domain. With this motivation, this paper presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This feature extractor has three main contributions: First, it proposes to identify salient subregions in an image, based on domain-specific prior knowledge, and to quantify the image by employing only the characteristics of these subregions instead of considering the characteristics of all image locations. Second, it introduces a new deep learning-based technique that quantizes the salient subregions by extracting a set of features directly learned on image data and uses the distribution of these quantizations for image representation and classification. To this end, the proposed deep learning-based technique constructs a deep belief network of the restricted Boltzmann machines (RBMs), defines the activation values of the hidden unit nodes in the final RBM as the features, and learns the quantizations by clustering these features in an unsupervised way. Third, this extractor is the first example for successfully using the restricted Boltzmann machines in the domain of histopathological image analysis. Our experiments on microscopic colon tissue images reveal that the proposed feature extractor is effective to obtain more accurate classification results compared to its counterparts.
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Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M, Turk P, Bera K, Abraham J, Sikov WM, Somlo G, Harris LN, Gilmore H, Plecha D, Varadan V, Madabhushi A. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer. JAMA Netw Open 2019; 2:e192561. [PMID: 31002322 PMCID: PMC6481453 DOI: 10.1001/jamanetworkopen.2019.2561] [Citation(s) in RCA: 182] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
IMPORTANCE There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer. OBJECTIVE To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy. DESIGN, SETTING, AND PARTICIPANTS In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019. MAIN OUTCOMES AND MEASURES Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting. RESULTS In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002). CONCLUSIONS AND RELEVANCE A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.
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Affiliation(s)
- Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Salendra Singh
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Maryam Etesami
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gallagher
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - B. Nicolas Bloch
- Department of Radiology, Boston Medical Center, Boston, Massachusetts
- Department of Radiology, Boston University School of Medicine, Boston, Massachusetts
| | - Manasa Vulchi
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - Paulette Turk
- Department of Diagnostic Radiology, The Cleveland Clinic, Cleveland, Ohio
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jame Abraham
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - William M. Sikov
- Program in Women’s Oncology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - George Somlo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California
| | - Lyndsay N. Harris
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Hannah Gilmore
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Donna Plecha
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Vinay Varadan
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
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Corredor G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, Rimm DL, Yang M, Romero E, Schalper KA, Velcheti V, Madabhushi A. Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non-Small Cell Lung Cancer. Clin Cancer Res 2019; 25:1526-1534. [PMID: 30201760 PMCID: PMC6397708 DOI: 10.1158/1078-0432.ccr-18-2013] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/16/2018] [Accepted: 09/06/2018] [Indexed: 12/16/2022]
Abstract
PURPOSE The presence of a high degree of tumor-infiltrating lymphocytes (TIL) has been proven to be associated with outcome in patients with non-small cell lung cancer (NSCLC). However, recent evidence indicates that tissue architecture is also prognostic of disease-specific survival and recurrence. We show a set of descriptors (spatial TIL, SpaTIL) that capture density, and spatial colocalization of TILs and tumor cells across digital images that can predict likelihood of recurrence in early-stage NSCLC. EXPERIMENTAL DESIGN The association between recurrence in early-stage NSCLC and SpaTIL features was explored on 301 patients across four different cohorts. Cohort D1 (n = 70) was used to identify the most prognostic SpaTIL features and to train a classifier to predict the likelihood of recurrence. The classifier performance was evaluated in cohorts D2 (n = 119), D3 (n = 112), and D4 (n = 112). Two pathologists graded each sample of D1 and D2; intraobserver agreement and association between manual grading and likelihood of recurrence were analyzed. RESULTS SpaTIL was associated with likelihood of recurrence in all test sets (log-rank P < 0.02). A multivariate Cox proportional hazards analysis revealed an HR of 3.08 (95% confidence interval, 2.1-4.5, P = 7.3 × 10-5). In contrast, agreement among expert pathologists using tumor grade was moderate (Kappa = 0.5), and the manual TIL grading was only prognostic for one reader in D2 (P = 8.0 × 10-3). CONCLUSIONS A set of features related to density and spatial architecture of TILs was found to be associated with a likelihood of recurrence of early-stage NSCLC. This information could potentially be used for helping in treatment planning and management of early-stage NSCLC.See related commentary by Peled et al., p. 1449.
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Affiliation(s)
- Germán Corredor
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Xiangxue Wang
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
| | - Yu Zhou
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
| | - Cheng Lu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Konstantinos Syrigos
- Department of Medicine, University of Athens, Sotiria General Hospital, Athens, Greece
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Michael Yang
- Department of Pathology-Anatomic, University Hospitals, Cleveland, Ohio
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Kurt A Schalper
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Vamsidhar Velcheti
- Hematology and Medical Oncology Department, Cleveland Clinic, Cleveland, Ohio
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio.
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Lan C, Li J, Huang X, Heindl A, Wang Y, Yan S, Yuan Y. Stromal cell ratio based on automated image analysis as a predictor for platinum-resistant recurrent ovarian cancer. BMC Cancer 2019; 19:159. [PMID: 30777045 PMCID: PMC6380057 DOI: 10.1186/s12885-019-5343-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 02/01/2019] [Indexed: 02/03/2023] Open
Abstract
Background Identifying high-risk patients for platinum resistance is critical for improving clinical management of ovarian cancer. We aimed to use automated image analysis of hematoxylin & eosin (H&E) stained sections to identify the association between microenvironmental composition and platinum-resistant recurrent ovarian cancer. Methods Ninety-one patients with ovarian cancer containing the data of automated image analysis for H&E histological sections were initially reviewed. Results Seventy-one patients with recurrent disease were finally identified. Among 30 patients with high stromal cell ratio, 60% of the patients had platinum-resistant recurrence, which was significantly higher than the rate in patients with low stromal cell ratio (9.80%, P < 0.001). Multivariate logistic regression analysis revealed elevated CA125 level after 3 cycles of chemotherapy (P < 0.001) and high stromal cell ratio (P = 0.002) were the negative predictors of platinum-resistant relapse. The area under the curve (AUC) of receiver operating characteristic (ROC) curves of the models for predicting platinum-resistant recurrence with stromal cell ratio, normalization of CA125 level, and the combination of two parameters were 0.78, 0.79, and 0.89 respectively. Conclusions Our results demonstrated stromal cell ratio based on automated image analysis may be a potential predictor for ovarian cancer patients at high risk of platinum-resistant recurrence, and it could improve the predictive value of model when combined with normalization of CA125 level after 3 cycles of chemotherapy.
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Affiliation(s)
- C Lan
- Department of Gynecologic Oncology, Sun Yat-sen University Cancer Centre, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, China
| | - J Li
- Department of Gynecologic Oncology, Sun Yat-sen University Cancer Centre, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, China
| | - X Huang
- Department of Gynecologic Oncology, Sun Yat-sen University Cancer Centre, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, China
| | - A Heindl
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.,Centre for Molecular Pathology, The Royal Marsden Hospital, London, UK.,Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Y Wang
- Department of Gynecologic Oncology, Sun Yat-sen University Cancer Centre, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, China
| | - S Yan
- Department of Pathology, Sun Yat-sen University Cancer Centre, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, China
| | - Y Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. .,Centre for Molecular Pathology, The Royal Marsden Hospital, London, UK. .,Division of Molecular Pathology, The Institute of Cancer Research, London, UK.
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