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Brevet M, Li Z, Parwani A. Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges. J Pathol Inform 2024; 15:100343. [PMID: 38125925 PMCID: PMC10730362 DOI: 10.1016/j.jpi.2023.100343] [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: 08/25/2023] [Revised: 09/18/2023] [Accepted: 11/01/2023] [Indexed: 12/23/2023] Open
Abstract
For the past 2 decades, pathologists have been accustomed to reporting the HER2 status of breast cancer as either positive or negative, based on HER2 IHC. Today, however, there is a clinical imperative to employ a 3-tier approach to interpreting HER2 IHC that can also identify tumours categorised as HER2-low. Meeting this need for a finer degree of discrimination may be challenging, and in this article, we consider the potential for the integration of computational approaches to support pathologists in achieving accurate and reproducible HER2 IHC scoring as well as outlining some of the practicalities involved.
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Affiliation(s)
| | - Zaibo Li
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
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2
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Kaur J, Kaur P. A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques. Comput Biol Med 2024; 179:108910. [PMID: 39032244 DOI: 10.1016/j.compbiomed.2024.108910] [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: 04/13/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/23/2024]
Abstract
Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure for segmentation and classification with a large set of data modalities can be a challenging task. Therefore, a crucial requirement is to significantly develop the computer-assisted diagnostic system intended for the initial cancer identification. This article offers a systematic review of Deep Learning approaches using various image modalities to detect multi-organ cancers from 2012 to 2023. It emphasizes the detection of five supreme predominant tumors, i.e., breast, brain, lung, skin, and liver. Extensive review has been carried out by collecting research and conference articles and book chapters from reputed international databases, i.e., Springer Link, IEEE Xplore, Science Direct, PubMed, and Wiley that fulfill the criteria for quality evaluation. This systematic review summarizes the overview of convolutional neural network model architectures and datasets used for identifying and classifying the diverse categories of cancer. This study accomplishes an inclusive idea of ensemble deep learning models that have achieved better evaluation results for classifying the different images into cancer or healthy cases. This paper will provide a broad understanding to the research scientists within the domain of medical imaging procedures of which deep learning technique perform best over which type of dataset, extraction of features, different confrontations, and their anticipated solutions for the complex problems. Lastly, some challenges and issues which control the health emergency have been discussed.
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Affiliation(s)
- Jaspreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab, India.
| | - Prabhpreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab, India.
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3
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Weng W, Yoshida N, Morinaga Y, Sugino S, Tomita Y, Kobayashi R, Inoue K, Hirose R, Dohi O, Itoh Y, Zhu X. Development of high-quality artificial intelligence for computer-aided diagnosis in determining subtypes of colorectal cancer. J Gastroenterol Hepatol 2024. [PMID: 38923607 DOI: 10.1111/jgh.16661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/14/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND AND AIM There are no previous studies in which computer-aided diagnosis (CAD) diagnosed colorectal cancer (CRC) subtypes correctly. In this study, we developed an original CAD for the diagnosis of CRC subtypes. METHODS Pretraining for the CAD based on ResNet was performed using ImageNet and five open histopathological pretraining image datasets (HiPreD) containing 3 million images. In addition, sparse attention was introduced to improve the CAD compared to other attention networks. One thousand and seventy-two histopathological images from 29 early CRC cases at Kyoto Prefectural University of Medicine from 2019 to 2022 were collected (857 images for training and validation, 215 images for test). All images were annotated by a qualified histopathologist for segmentation of normal mucosa, adenoma, pure well-differentiated adenocarcinoma (PWDA), and moderately/poorly differentiated adenocarcinoma (MPDA). Diagnostic ability including dice sufficient coefficient (DSC) and diagnostic accuracy were evaluated. RESULTS Our original CAD, named Colon-seg, with the pretraining of both HiPreD and ImageNET showed a better DSC (88.4%) compared to CAD without both pretraining (76.8%). Regarding the attentional mechanism, Colon-seg with sparse attention showed a better DSC (88.4%) compared to other attentional mechanisms (dual: 79.7%, ECA: 80.7%, shuffle: 84.7%, SK: 86.9%). In addition, the DSC of Colon-seg (88.4%) was better than other types of CADs (TransUNet: 84.7%, MultiResUnet: 86.1%, Unet++: 86.7%). The diagnostic accuracy of Colon-seg for each histopathological type was 94.3% for adenoma, 91.8% for PWDA, and 92.8% for MPDA. CONCLUSION A deep learning-based CAD for CRC subtype differentiation was developed with pretraining and fine-tuning of abundant histopathological images.
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Affiliation(s)
- Weihao Weng
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan
| | - Naohisa Yoshida
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yukiko Morinaga
- Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Satoshi Sugino
- Department of Gastroenterology, Asahi University Hospital, Gifu, Japan
| | - Yuri Tomita
- Department of Gastroenterology, Koseikai Takeda Hospital, Kyoto, Japan
| | - Reo Kobayashi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ken Inoue
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ryohei Hirose
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Osamu Dohi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yoshito Itoh
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Xin Zhu
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan
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Faur AC, Buzaș R, Lăzărescu AE, Ghenciu LA. Current Developments in Diagnosis of Salivary Gland Tumors: From Structure to Artificial Intelligence. Life (Basel) 2024; 14:727. [PMID: 38929710 PMCID: PMC11204840 DOI: 10.3390/life14060727] [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: 04/26/2024] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Salivary glands tumors are uncommon neoplasms with variable incidence, heterogenous histologies and unpredictable biological behaviour. Most tumors are located in the parotid gland. Benign salivary tumors represent 54-79% of cases and pleomorphic adenoma is frequently diagnosed in this group. Salivary glands malignant tumors that are more commonly diagnosed are adenoid cystic carcinomas and mucoepidermoid carcinomas. Because of their diversity and overlapping features, these tumors require complex methods of evaluation. Diagnostic procedures include imaging techniques combined with clinical examination, fine needle aspiration and histopathological investigation of the excised specimens. This narrative review describes the advances in the diagnosis methods of these unusual tumors-from histomorphology to artificial intelligence algorithms.
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Affiliation(s)
- Alexandra Corina Faur
- Department of Anatomy and Embriology, ”Victor Babeș” University of Medicine and Pharmacy, Eftimie Murgu Square, No. 2, 300041 Timișoara, Romania; (A.C.F.); (A.E.L.)
| | - Roxana Buzaș
- Department of Internal Medicine I, Center for Advanced Research in Cardiovascular Pathology and Hemostaseology, ”Victor Babeș” University of Medicine and Pharmacy, Eftimie Murgu Square, No. 2, 300041 Timișoara, Romania
| | - Adrian Emil Lăzărescu
- Department of Anatomy and Embriology, ”Victor Babeș” University of Medicine and Pharmacy, Eftimie Murgu Square, No. 2, 300041 Timișoara, Romania; (A.C.F.); (A.E.L.)
| | - Laura Andreea Ghenciu
- Department of Functional Sciences, ”Victor Babeș”University of Medicine and Pharmacy, Eftimie Murgu Square, No. 2, 300041 Timișoara, Romania;
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Proffer SL, Reinhart J, Ridgeway JL, Barry B, Kamath C, Gerdes EW, Todd A, Cervenka DJ, DiCaudo DJ, Sokumbi O, Johnson EF, Peters MS, Wieland CN, Comfere NI. Digital dermatopathology implementation: Experience at a multisite academic institution. J Cutan Pathol 2024. [PMID: 38783791 DOI: 10.1111/cup.14629] [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: 01/08/2024] [Revised: 04/01/2024] [Accepted: 04/13/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Technology has revolutionized not only direct patient care but also diagnostic care processes. This study evaluates the transition from glass-slide microscopy to digital pathology (DP) at a multisite academic institution, using mixed methods to understand user perceptions of digitization and key productivity metrics of practice change. METHODS Participants included dermatopathologists, pathology reporting specialists, and clinicians. Electronic surveys and individual or group interviews included questions related to technology comfort, trust in DP, and rationale for DP adoption. Case volumes and turnaround times were abstracted from the electronic health record from Qtr 4 2020 to Qtr 1 2023 (inclusive). Data were analyzed descriptively, while interviews were analyzed using methods of content analysis. RESULTS Thirty-four staff completed surveys and 22 participated in an interview. Case volumes and diagnostic turnaround time did not differ across the institution during or after implementation timelines (p = 0.084; p = 0.133, respectively). 82.5% (28/34) of staff agreed that DP improved the sign-out experience, with accessibility, ergonomics, and annotation features described as key factors. Clinicians reported positive perspectives of DP impact on patient safety and interdisciplinary collaboration. CONCLUSIONS Our study demonstrates that DP has a high acceptance rate, does not adversely impact productivity, and may improve patient safety and care collaboration.
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Affiliation(s)
- Sydney L Proffer
- Department of Dermatology, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Jacob Reinhart
- Department of Dermatology, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Jennifer L Ridgeway
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Barbara Barry
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Celia Kamath
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Erin Wissler Gerdes
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Austin Todd
- Division of Clinical Trials and Biostatistics of the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Derek J Cervenka
- Department of Dermatology, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - David J DiCaudo
- Department of Dermatology, Mayo Clinic, Scottsdale, Arizona, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Arizona, USA
| | | | - Emma F Johnson
- Department of Dermatology, Mayo Clinic Rochester, Rochester, Minnesota, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Margot S Peters
- Department of Dermatology, Mayo Clinic Rochester, Rochester, Minnesota, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Carilyn N Wieland
- Department of Dermatology, Mayo Clinic Rochester, Rochester, Minnesota, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Nneka I Comfere
- Department of Dermatology, Mayo Clinic Rochester, Rochester, Minnesota, USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
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Ivanova M, Pescia C, Trapani D, Venetis K, Frascarelli C, Mane E, Cursano G, Sajjadi E, Scatena C, Cerbelli B, d’Amati G, Porta FM, Guerini-Rocco E, Criscitiello C, Curigliano G, Fusco N. Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence. Cancers (Basel) 2024; 16:1981. [PMID: 38893102 PMCID: PMC11171409 DOI: 10.3390/cancers16111981] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Abstract
Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.
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Affiliation(s)
- Mariia Ivanova
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Carlo Pescia
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Dario Trapani
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Konstantinos Venetis
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Chiara Frascarelli
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Eltjona Mane
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Giulia Cursano
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Elham Sajjadi
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Cristian Scatena
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy;
| | - Bruna Cerbelli
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, Italy;
| | - Giulia d’Amati
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy;
| | - Francesca Maria Porta
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Elena Guerini-Rocco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Nicola Fusco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
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McCaffrey C, Jahangir C, Murphy C, Burke C, Gallagher WM, Rahman A. Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer. Expert Rev Mol Diagn 2024; 24:363-377. [PMID: 38655907 DOI: 10.1080/14737159.2024.2346545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/19/2024] [Indexed: 04/26/2024]
Abstract
INTRODUCTION Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.
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Affiliation(s)
- Christine McCaffrey
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Chowdhury Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
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Hagi T, Nakamura T, Yuasa H, Uchida K, Asanuma K, Sudo A, Wakabayahsi T, Morita K. Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas. Cancer Med 2024; 13:e7252. [PMID: 38800990 PMCID: PMC11129162 DOI: 10.1002/cam4.7252] [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: 09/06/2023] [Revised: 04/01/2024] [Accepted: 04/28/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS. METHODS Our retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4-fold validation. RESULTS The cohort included 35 patients with a mean age of 64 years, and the mean follow-up period was 34 months (2-66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis-free survival, the accuracy was 84.2% with the AUC of 0.852. CONCLUSION AI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS.
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Affiliation(s)
- Tomohito Hagi
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Tomoki Nakamura
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Hiroto Yuasa
- Department of Oncologic PathologyMie University Graduate School of MedicineTsuJapan
| | - Katsunori Uchida
- Department of Oncologic PathologyMie University Graduate School of MedicineTsuJapan
| | - Kunihiro Asanuma
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Akihiro Sudo
- Department of Orthopedic SurgeryMie University Graduate School of MedicineTsuJapan
| | - Tetsushi Wakabayahsi
- Department of Information EngineeringMie University Graduate School of EngineeringTsuJapan
| | - Kento Morita
- Department of Information EngineeringMie University Graduate School of EngineeringTsuJapan
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Katayama A, Aoki Y, Watanabe Y, Horiguchi J, Rakha EA, Oyama T. Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers. Int J Clin Oncol 2024:10.1007/s10147-024-02513-3. [PMID: 38619651 DOI: 10.1007/s10147-024-02513-3] [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: 01/16/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.
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Affiliation(s)
- Ayaka Katayama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan.
| | - Yuki Aoki
- Center for Mathematics and Data Science, Gunma University, Maebashi, Japan
| | - Yukako Watanabe
- Clinical Training Center, Gunma University Hospital, Maebashi, Japan
| | - Jun Horiguchi
- Department of Breast Surgery, International University of Health and Welfare, Narita, Japan
| | - Emad A Rakha
- Department of Histopathology School of Medicine, University of Nottingham, University Park, Nottingham, UK
- Department of Pathology, Hamad Medical Corporation, Doha, Qatar
| | - Tetsunari Oyama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan
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10
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Sajithkumar A, Thomas J, Saji AM, Ali F, E K HH, Adampulan HAG, Sarathchand S. Artificial Intelligence in pathology: current applications, limitations, and future directions. Ir J Med Sci 2024; 193:1117-1121. [PMID: 37542634 DOI: 10.1007/s11845-023-03479-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE Given AI's recent success in computer vision applications, majority of pathologists anticipate that it will be able to assist them with a variety of digital pathology activities. Massive improvements in deep learning have enabled a synergy between Artificial Intelligence (AI) and deep learning, enabling image-based diagnosis against the backdrop of digital pathology. AI-based solutions are being developed to eliminate errors and save pathologists time. AIMS In this paper, we will discuss the components that went into the use of Artificial Intelligence in Pathology, its use in the medical profession, the obstacles and constraints that it encounters, and the future possibilities of AI in the medical field. CONCLUSIONS Based on these factors, we elaborate upon the use of AI in medical pathology and provide future recommendations for its successful implementation in this field.
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Affiliation(s)
- Akhil Sajithkumar
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India.
| | - Jubin Thomas
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Ajish Meprathumalil Saji
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Fousiya Ali
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Haneena Hasin E K
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Hannan Abdul Gafoor Adampulan
- Department of Oral Pathology and Microbiology, Malabar Dental College and Research Centre, Manoor Chekanoor Road, Mudur PO, Edappal, Malappuram Dist, 679578, India
| | - Swathy Sarathchand
- Sree Narayana Institute of Medical Sciences, Chalakka - Kuthiathode Rd, North Kuthiathode, Kunnukara, Kerala, 683594, India
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11
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Ibrahim A, Jahanifar M, Wahab N, Toss MS, Makhlouf S, Atallah N, Lashen AG, Katayama A, Graham S, Bilal M, Bhalerao A, Ahmed Raza SE, Snead D, Minhas F, Rajpoot N, Rakha E. Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application. Mod Pathol 2024; 37:100416. [PMID: 38154653 DOI: 10.1016/j.modpat.2023.100416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/27/2023] [Accepted: 12/14/2023] [Indexed: 12/30/2023]
Abstract
In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.
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Affiliation(s)
- Asmaa Ibrahim
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Department of Pathology, Faculty of Medicine, Suez Canal University, Egypt
| | - Mostafa Jahanifar
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Noorul Wahab
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Michael S Toss
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Histopathology Department, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Shorouk Makhlouf
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Nehal Atallah
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Ayat G Lashen
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Ayaka Katayama
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Simon Graham
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Mohsin Bilal
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Abhir Bhalerao
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Shan E Ahmed Raza
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - David Snead
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, United Kingdom
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, United Kingdom.
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom; Pathology Department, Hamad Medical Corporation, Doha, Qatar.
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12
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Greenberg A, Samueli B, Farkash S, Zohar Y, Ish-Shalom S, Hagege RR, Hershkovitz D. Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners. Diagn Pathol 2024; 19:26. [PMID: 38321431 PMCID: PMC10845737 DOI: 10.1186/s13000-024-01452-x] [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: 11/05/2023] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. METHODS The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. RESULTS The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. CONCLUSIONS The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.
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Affiliation(s)
- Ariel Greenberg
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
| | - Benzion Samueli
- Department of Pathology, Soroka University Medical Center, 76 Wingate Street, 8486614, Be'er Sheva, Israel
| | - Shai Farkash
- Department of Pathology, Emek Medical Center, Yitshak Rabin Boulevard 21, 1834111, Afula, Israel
| | - Yaniv Zohar
- Department of Pathology, Rambam Medical Center, 8 Haalia Hashnia, 3525408, Haifa, Israel
| | - Shahar Ish-Shalom
- Department of Pathology, Kaplan Medical Center, Pasternak St. P.O.B. 1, 76100, Rehovot, Israel
| | - Rami R Hagege
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Dov Hershkovitz
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69978, Tel-Aviv, Israel
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13
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Zhu Q, Dai H, Qiu F, Lou W, Wang X, Deng L, Shi C. Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer. Transl Oncol 2024; 40:101855. [PMID: 38185058 PMCID: PMC10808968 DOI: 10.1016/j.tranon.2023.101855] [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: 09/06/2023] [Revised: 11/27/2023] [Accepted: 11/30/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence. METHODS We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance. RESULTS Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients. CONCLUSION Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.
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Affiliation(s)
- Qiuli Zhu
- Department of Genetics, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hua Dai
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Feng Qiu
- Department of Oncology, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, No.7889 of Changdong avenue, Gaoxin District, Nanchang, Jiangxi, China
| | - Weiming Lou
- The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xin Wang
- Queen Mary School of Nanchang University, Nanchang University, Nanchang, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China.
| | - Chao Shi
- Department of Oncology, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, No.7889 of Changdong avenue, Gaoxin District, Nanchang, Jiangxi, China.
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14
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Hurley NC, Gupta RK, Schroeder KM, Hess AS. Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia? Reg Anesth Pain Med 2024:rapm-2023-104868. [PMID: 38253610 DOI: 10.1136/rapm-2023-104868] [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: 07/17/2023] [Accepted: 09/18/2023] [Indexed: 01/24/2024]
Abstract
INTRODUCTION Artificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in performing guideline-based evaluation of neuraxial bleeding risk in hypothetical patients on anticoagulation medication. The study also explored the impact of structured prompt guidance on the LLM's performance. METHODS A dataset of 10 hypothetical patient stems and 26 anticoagulation profiles (260 unique combinations) was developed based on American Society of Regional Anesthesia and Pain Medicine guidelines. Five prompts were created for the LLM, ranging from minimal guidance to explicit instructions. The model's responses were compared with a "truth table" based on the guidelines. Performance metrics, including accuracy and area under the receiver operating curve (AUC), were used. RESULTS Baseline performance of GPT-3.5 was slightly above chance. With detailed prompts and explicit guidelines, performance improved significantly (AUC 0.70, 95% CI (0.64 to 0.77)). Performance varied among medication classes. DISCUSSION LLMs show potential for assisting in clinical decision making but rely on accurate and relevant prompts. Integration of LLMs should consider safety and privacy concerns. Further research is needed to optimize LLM performance and address complex scenarios. The tested LLM demonstrates potential in assessing neuraxial bleeding risk but relies on precise prompts. LLM integration should be approached cautiously, considering limitations. Future research should focus on optimization and understanding LLM capabilities and limitations in healthcare.
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Affiliation(s)
- Nathan C Hurley
- Department of Anesthesiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Rajnish K Gupta
- Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Aaron S Hess
- Department of Anesthesiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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15
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TerKonda SP, TerKonda AA, Sacks JM, Kinney BM, Gurtner GC, Nachbar JM, Reddy SK, Jeffers LL. Artificial Intelligence: Singularity Approaches. Plast Reconstr Surg 2024; 153:204e-217e. [PMID: 37075274 DOI: 10.1097/prs.0000000000010572] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
SUMMARY Artificial intelligence (AI) has been a disruptive technology within health care, from the development of simple care algorithms to complex deep-learning models. AI has the potential to reduce the burden of administrative tasks, advance clinical decision-making, and improve patient outcomes. Unlocking the full potential of AI requires the analysis of vast quantities of clinical information. Although AI holds tremendous promise, widespread adoption within plastic surgery remains limited. Understanding the basics is essential for plastic surgeons to evaluate the potential uses of AI. This review provides an introduction of AI, including the history of AI, key concepts, applications of AI in plastic surgery, and future implications.
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Affiliation(s)
- Sarvam P TerKonda
- From the Division of Plastic and Reconstructive Surgery, Mayo Clinic Florida
| | - Anurag A TerKonda
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Justin M Sacks
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Brian M Kinney
- Division of Plastic Surgery, University of Southern California
| | - Geoff C Gurtner
- Division of Plastic and Reconstructive Surgery, Stanford University
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16
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Jiang VS, Pavlovic ZJ, Hariton E. The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies. Obstet Gynecol Clin North Am 2023; 50:747-762. [PMID: 37914492 DOI: 10.1016/j.ogc.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.
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Affiliation(s)
- Victoria S Jiang
- Division of Reproductive Endocrinology & Infertility, Vincent Department of Obstetrics and Gynecology, Massachusetts General Hospital/Harvard Medical School, 55 Fruit Street, Suite 10A, Boston, MA 02116, USA
| | - Zoran J Pavlovic
- Department of Obstetrics and Gynecology/Reproductive Endocrinology and Infertility, University of South Florida, Morsani College of Medicine, 2 Tampa General Circle, 6th Floor, Suite 6022, Tampa, FL 33602, USA
| | - Eduardo Hariton
- Reproductive Science Center of the San Francisco Bay Area, 100 Park Place #200, San Ramon, CA 94583, USA.
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17
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Goel A, Kapatia G, Parwaiz A, Gupta S. Commentary on "Comparison of glass and digital slides for cervical cytopathology screening and interpretation". Diagn Cytopathol 2023; 51:791-792. [PMID: 37828831 DOI: 10.1002/dc.25242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023]
Affiliation(s)
- Archit Goel
- Department of Pathology, All India Institute of Medical Sciences (AIIMS), Bathinda, India
| | - Gargi Kapatia
- Department of Pathology, All India Institute of Medical Sciences (AIIMS), Bathinda, India
| | - Amber Parwaiz
- Department of Pathology, All India Institute of Medical Sciences (AIIMS), Patna, India
| | - Shruti Gupta
- Department of Pathology, All India Institute of Medical Sciences (AIIMS), Rae Bareli, India
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18
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de Velozo G, Cordeiro J, Sousa J, Holanda AC, Pessoa G, Porfírio M, Távora F. Comparison of glass and digital slides for cervical cytopathology screening and interpretation. Diagn Cytopathol 2023; 51:735-743. [PMID: 37587842 DOI: 10.1002/dc.25209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/18/2023]
Abstract
Cervical cancer is the second most common form of cancer and a leading cause of premature death among women aged 15 to 44 worldwide. In Brazil, there is a high prevalence of infection by the human papillomavirus - HPV. Digital pathology optimizes time and space for reading cervicovaginal cytology slides. We evaluated the feasibility of using whole slide images (WSI) for the routine interpretation of cytology exams. A total of 99 cases of vaginal cytology were selected from a reference laboratory in Northeastern Brazil. Three cytotechnicians participated in the study. Cellular atypia was the one that most presented concordance values. Two observers almost perfectly agreed (k = 0.86 and k = 0.84, respectively) on the negative diagnoses. The performance of the evaluators for NILM (negative for intraepithelial lesion and malignancy) showed high reproducibility and sensitivity in the digital slides, mainly between evaluators A and C. In contrast, the microbiology group showed disagreement between the diagnoses by digital slides and the standard- gold. The concordance between the digital diagnoses and the gold standard for ASCUS was 89%. In the inflammatory category, Spearman's test showed similar results between raters A, B, and C (rs = 0.47, rs = 0.41, and rs = 0.47, respectively). This study reports the diagnostic validation using digital slides in view of the need to optimize the cytology visualization process. Our experience shows good diagnostic agreement between digital and optical microscopy in several analyzed categories, but mainly in relation to cellular atypia and inflammatory processes.
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Affiliation(s)
| | - Juliana Cordeiro
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
| | | | | | | | - Mônica Porfírio
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
| | - Fábio Távora
- Federal University of Ceará, Argos Patologia Laboratory, Fortaleza, Brazil
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19
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Chatterji S, Niehues JM, van Treeck M, Loeffler CML, Saldanha OL, Veldhuizen GP, Cifci D, Carrero ZI, Abu-Eid R, Speirs V, Kather JN. Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer. NPJ Breast Cancer 2023; 9:91. [PMID: 37940649 PMCID: PMC10632426 DOI: 10.1038/s41523-023-00599-y] [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: 05/29/2023] [Accepted: 10/27/2023] [Indexed: 11/10/2023] Open
Abstract
Breast cancer prognosis and management for both men and women are reliant upon estrogen receptor alpha (ERα) and progesterone receptor (PR) expression to inform therapy. Previous studies have shown that there are sex-specific binding characteristics of ERα and PR in breast cancer and, counterintuitively, ERα expression is more common in male than female breast cancer. We hypothesized that these differences could have morphological manifestations that are undetectable to human observers but could be elucidated computationally. To investigate this, we trained attention-based multiple instance learning prediction models for ERα and PR using H&E-stained images of female breast cancer from the Cancer Genome Atlas (TCGA) (n = 1085) and deployed them on external female (n = 192) and male breast cancer images (n = 245). Both targets were predicted in the internal (AUROC for ERα prediction: 0.86 ± 0.02, p < 0.001; AUROC for PR prediction = 0.76 ± 0.03, p < 0.001) and external female cohorts (AUROC for ERα prediction: 0.78 ± 0.03, p < 0.001; AUROC for PR prediction = 0.80 ± 0.04, p < 0.001) but not the male cohort (AUROC for ERα prediction: 0.66 ± 0.14, p = 0.43; AUROC for PR prediction = 0.63 ± 0.04, p = 0.05). This suggests that subtle morphological differences invisible upon visual inspection may exist between the sexes, supporting previous immunohistochemical, genomic, and transcriptomic analyses.
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Affiliation(s)
- Subarnarekha Chatterji
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Aberdeen Cancer Centre, University of Aberdeen, Aberdeen, UK
| | - Jan Moritz Niehues
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Marko van Treeck
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Chiara Maria Lavinia Loeffler
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
- Department of Medicine I, University Hospital and Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Oliver Lester Saldanha
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Gregory Patrick Veldhuizen
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Didem Cifci
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
| | - Zunamys Itzell Carrero
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Rasha Abu-Eid
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
- Aberdeen Cancer Centre, University of Aberdeen, Aberdeen, UK
- Institute of Dentistry, University of Aberdeen, Aberdeen, UK
| | - Valerie Speirs
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK.
- Aberdeen Cancer Centre, University of Aberdeen, Aberdeen, UK.
| | - Jakob Nikolas Kather
- Else Kröner Fresenius Centre for Digital Health, Carl Gustav Carus Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Department of Medicine III, University Hospital RWTH (Rheinisch-Westfälische Technische Hochschule) Aachen, Aachen, Germany
- Department of Medicine I, University Hospital and Faculty of Medicine, Technical University of Dresden, Dresden, Germany
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
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20
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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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21
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Deng XY, Cao PW, Nan SM, Pan YP, Yu C, Pan T, Dai G. Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study. Clin Breast Cancer 2023; 23:729-736. [PMID: 37481337 DOI: 10.1016/j.clbc.2023.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/24/2023]
Abstract
OBJECTIVE To investigate the diagnostic performance of a mammography-based radiomics model for distinguishing phyllodes tumors (PTs) from fibroadenomas (FAs) of the breast. MATERIALS AND METHODS A total of 156 patients were retrospectively included (75 with PTs, 81 with FAs) and divided into training and validation groups at a ratio of 7:3. Radiomics features were extracted from craniocaudal and mediolateral oblique images. The least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were performed to select features. Three machine learning classifiers, including logistic regression (LR), K-nearest neighbor classifier (KNN) and support vector machine (SVM), were implemented in the radiomics model, imaging model and combined model. Receiver operating characteristic curves, area under the curve (AUC), sensitivity and specificity were computed. RESULTS Among 1084 features, the LASSO algorithm selected 17 features, and PCA further selected 6 features. Three machine learning classifiers yielded the same AUC of 0.935 in the validation group for the radiomics model. In the imaging model, KNN yielded the highest accuracy rate of 89.4% and AUC of 0.947 in the validation set. For the combined model, the SVM classifier reached the highest AUC of 0.918 with an accuracy rate of 86.2%, sensitivity of 83.9%, and specificity of 89.4% in the training group. In the validation group, LR yielded the highest AUC of 0.973. The combined model had a relatively higher AUC than the radiomics model or imaging model, especially in the validation group. CONCLUSIONS Mammography-based radiomics features demonstrate good diagnostic performance for discriminating PTs from FAs.
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Affiliation(s)
- Xue-Ying Deng
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
| | - Pei-Wei Cao
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Shuai-Ming Nan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yue-Peng Pan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Chang Yu
- Department of Pathology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Ting Pan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Gang Dai
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
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22
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Schlam I, Saad Menezes MC, Corti C, Tan A, Abuali I, Tolaney SM. Artificial intelligence as an adjunct tool for breast oncologists - are we there yet? ESMO Open 2023; 8:101643. [PMID: 37703594 PMCID: PMC10502370 DOI: 10.1016/j.esmoop.2023.101643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Affiliation(s)
- I Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston; Harvard T.H. Chan School of Public Health, Boston.
| | - M C Saad Menezes
- Harvard T.H. Chan School of Public Health, Boston; Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - C Corti
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan; Department of Oncology and Hemato-Oncology (DIPO), University of Milan, Milan, Italy
| | - A Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - I Abuali
- Department of Hematology and Oncology, Massachusetts General Hospital, Boston
| | - S M Tolaney
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston; Harvard Medical School, Boston, USA
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23
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Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S, Paladini A, Sonam F, Bendari A, Perkash RS, Anjali F, Varrassi G. Digital Pathology: Transforming Diagnosis in the Digital Age. Cureus 2023; 15:e44620. [PMID: 37799211 PMCID: PMC10547926 DOI: 10.7759/cureus.44620] [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: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 10/07/2023] Open
Abstract
In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.
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Affiliation(s)
- Nfn Kiran
- Pathology and Laboratory Medicine, Staten Island University Hospital, New York, USA
| | - Fnu Sapna
- Pathology and Laboratory Medicine, Albert Einstein College of Medicine, New York, USA
| | - Fnu Kiran
- Pathology and Laboratory Medicine, University of Missouri School of Medicine, Columbia, USA
| | - Deepak Kumar
- Pathology and Laboratory Medicine, University of Missouri, Columbia, USA
| | - Fnu Raja
- Pathology and Laboratory Medicine, MetroHealth Medical Center, Cleveland, USA
| | - Sheena Shiwlani
- Pathology and Laboratory Medicine, Isra University, Karachi, PAK
- Pathology, Mount Sinai Hospital, New York, USA
| | - Antonella Paladini
- Clinical Medicine, Public Health and Life Science (MESVA), University of L'Aquila, L'Aquila, ITA
| | - Fnu Sonam
- Pathology and Laboratory Medicine, Liaquat University of Medical and Health Sciences, Sukkur, PAK
- Medicine, Mustafai Trust Central Hospital, Sukkur, PAK
| | - Ahmed Bendari
- Pathology and Laboratory Medicine, Lenox Hill Hospital, New York, USA
| | | | - Fnu Anjali
- Internal Medicine, Sakhi Baba General Hospital, Sukkur, PAK
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24
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Zhang X, Liu SS, Ma J, Qu W. Secretory leukocyte protease inhibitor (SLPI) in cancer pathophysiology: Mechanisms of action and clinical implications. Pathol Res Pract 2023; 248:154633. [PMID: 37356220 DOI: 10.1016/j.prp.2023.154633] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/17/2023] [Accepted: 06/18/2023] [Indexed: 06/27/2023]
Abstract
Cancer is a multifaceted disorder frequently linked to the dysregulation of several biological processes. The SLPI is a multifunctional protein involved in the modulation of immunological response and the inhibition of protease activities. SLPI acts as an inhibitor of proteases, exerts antibacterial properties, and suppresses the transcription of proinflammatory genes through the nuclear factor-kappa B (NF-κB) pathway. The role of this protein as a regulatory agent has been implicated in various types of cancer. Recent research has revealed that SLPI upregulation in cancer cells enhances the metastatic capacity of epithelial malignancies, indicating the deleterious effects of this protein. Furthermore, SLPI interacts intricately with other cancer-promoting factors, including matrix metalloproteinase-2 (MMP-2), MMP-9, the NF-κB and Akt pathways, and the p53-upregulated modulator of apoptosis (PUMA). This review provides an overview of the role of SLPI in cancer pathophysiology, emphasizing its expression in cancer cells and tissues, its potential as a prognostic biomarker, and its therapeutic promise as a target in cancer treatment. The mechanisms of SLPI action in cancer, including its anti-inflammatory effects, regulation of cell proliferation and angiogenesis, and modulation of the tumor microenvironment, have been investigated. The clinical implications of SLPI in cancer have been discussed, including its potential as a diagnostic and prognostic biomarker, its role in chemoresistance, and its therapeutic potential in several types of cancer, such as hepatocellular carcinoma (HCC), colorectal cancer (CRC), pancreatic cancer, head and neck squamous cell carcinoma (HNSCC), ovarian cancer (OvCa), prostate cancer (PC), gastric cancer (GC), breast cancer, and other cancers. In addition, we emphasized the significance of SLPI in cancer, which offers fresh perspectives on potential targets for cancer therapy.
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Affiliation(s)
- Xiaohua Zhang
- Department of Clinical Laboratory, the Second Hospital of Jilin University, Changchun 130000, China
| | - Shan Shan Liu
- Department of General Medicine, the Second Hospital of Jilin University, Changchun 130000, China.
| | - Jingru Ma
- Department of Clinical Laboratory, the Second Hospital of Jilin University, Changchun 130000, China
| | - Wei Qu
- Department of General Medicine, the Second Hospital of Jilin University, Changchun 130000, China
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25
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McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [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: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
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Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
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26
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Bujak J, Kłęk S, Balawejder M, Kociniak A, Wilkus K, Szatanek R, Orzeszko Z, Welanyk J, Torbicz G, Jęckowski M, Kucharczyk T, Wohadlo Ł, Borys M, Stadnik H, Wysocki M, Kayser M, Słomka ME, Kosmowska A, Horbacka K, Gach T, Markowska B, Kowalczyk T, Karoń J, Karczewski M, Szura M, Sanecka-Duin A, Blum A. Creating an Innovative Artificial Intelligence-Based Technology (TCRact) for Designing and Optimizing T Cell Receptors for Use in Cancer Immunotherapies: Protocol for an Observational Trial. JMIR Res Protoc 2023; 12:e45872. [PMID: 37440307 PMCID: PMC10375398 DOI: 10.2196/45872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND Cancer continues to be the leading cause of mortality in high-income countries, necessitating the development of more precise and effective treatment modalities. Immunotherapy, specifically adoptive cell transfer of T cell receptor (TCR)-engineered T cells (TCR-T therapy), has shown promise in engaging the immune system for cancer treatment. One of the biggest challenges in the development of TCR-T therapies is the proper prediction of the pairing between TCRs and peptide-human leukocyte antigen (pHLAs). Modern computational immunology, using artificial intelligence (AI)-based platforms, provides the means to optimize the speed and accuracy of TCR screening and discovery. OBJECTIVE This study proposes an observational clinical trial protocol to collect patient samples and generate a database of pHLA:TCR sequences to aid the development of an AI-based platform for efficient selection of specific TCRs. METHODS The multicenter observational study, involving 8 participating hospitals, aims to enroll patients diagnosed with stage II, III, or IV colorectal cancer adenocarcinoma. RESULTS Patient recruitment has recently been completed, with 100 participants enrolled. Primary tumor tissue and peripheral blood samples have been obtained, and peripheral blood mononuclear cells have been isolated and cryopreserved. Nucleic acid extraction (DNA and RNA) has been performed in 86 cases. Additionally, 57 samples underwent whole exome sequencing to determine the presence of somatic mutations and RNA sequencing for gene expression profiling. CONCLUSIONS The results of this study may have a significant impact on the treatment of patients with colorectal cancer. The comprehensive database of pHLA:TCR sequences generated through this observational clinical trial will facilitate the development of the AI-based platform for TCR selection. The results obtained thus far demonstrate successful patient recruitment and sample collection, laying the foundation for further analysis and the development of an innovative tool to expedite and enhance TCR selection for precision cancer treatments. TRIAL REGISTRATION ClinicalTrials.gov NCT04994093; https://clinicaltrials.gov/ct2/show/NCT04994093. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/45872.
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Affiliation(s)
- Joanna Bujak
- Ardigen SA, Cracow, Poland
- Department of Physics and Biophysics, Institute of Biology, Warsaw University of Life Sciences, Warszawa, Poland
| | - Stanisław Kłęk
- Surgical Oncology Clinic, Maria Sklodowska-Curie National Research Institute of Oncology, Cracow, Poland
| | | | | | | | | | - Zofia Orzeszko
- Department of General and Oncological Surgery, Brothers Hospitallers Hospital, Cracow, Poland
| | - Joanna Welanyk
- Surgical Oncology Clinic, Maria Sklodowska-Curie National Research Institute of Oncology, Cracow, Poland
| | - Grzegorz Torbicz
- Department of General Surgery and Surgical Oncology, Ludwik Rydygier Memorial Hospital, Cracow, Poland
| | - Mateusz Jęckowski
- Colon Cancer Unit, Department of Oncological Surgery, Voivodeship Multi-Specialist Center for Oncology and Traumatology, Lodz, Poland
| | - Tomasz Kucharczyk
- Holy Cross Cancer Center Clinic of Clinical Oncology, Cracow, Poland
| | - Łukasz Wohadlo
- Department of General Surgery, Andrzej Frycz Modrzewski Krakow University, Cracow, Poland
| | - Maciej Borys
- Department of General Surgery and Surgical Oncology, Ludwik Rydygier Memorial Hospital, Cracow, Poland
| | - Honorata Stadnik
- Department of General and Transplant Surgery, Poznan University of Medical Sciences, University Hospital, Poznan, Poland
| | - Michał Wysocki
- Department of General Surgery and Surgical Oncology, Ludwik Rydygier Memorial Hospital, Cracow, Poland
| | - Magdalena Kayser
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Marta Ewa Słomka
- Colon Cancer Unit, Department of Oncological Surgery, Voivodeship Multi-Specialist Center for Oncology and Traumatology, Lodz, Poland
| | - Anna Kosmowska
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Karolina Horbacka
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Tomasz Gach
- Surgical Clinic Institute of Physiotherapy, Faculty of Health Sciences, Jagiellonian University Medical College, Cracow, Poland
| | - Beata Markowska
- Surgical Clinic Institute of Physiotherapy, Faculty of Health Sciences, Jagiellonian University Medical College, Cracow, Poland
| | - Tomasz Kowalczyk
- Department of General Surgery, Andrzej Frycz Modrzewski Krakow University, Cracow, Poland
| | - Jacek Karoń
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Marek Karczewski
- Department of General and Transplant Surgery, Poznan University of Medical Sciences, University Hospital, Poznan, Poland
| | - Mirosław Szura
- Surgical Clinic Institute of Physiotherapy, Faculty of Health Sciences, Jagiellonian University Medical College, Cracow, Poland
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27
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Duenweg SR, Bobholz SA, Lowman AK, Stebbins MA, Winiarz A, Nath B, Kyereme F, Iczkowski KA, LaViolette PS. Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology. J Pathol Inform 2023; 14:100321. [PMID: 37496560 PMCID: PMC10365953 DOI: 10.1016/j.jpi.2023.100321] [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: 03/23/2023] [Revised: 06/13/2023] [Accepted: 06/28/2023] [Indexed: 07/28/2023] Open
Abstract
Purpose Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides. Design This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing. Results Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area. Conclusion This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.
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Affiliation(s)
- Savannah R. Duenweg
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Margaret A. Stebbins
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Biprojit Nath
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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28
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Stone A, Kalahiki C, Li L, Hubig N, Iuricich F, Dunn H. Evaluation of breast tumor morphologies from African American and Caucasian patients. Comput Struct Biotechnol J 2023; 21:3459-3465. [PMID: 38213888 PMCID: PMC10781995 DOI: 10.1016/j.csbj.2023.06.019] [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: 03/19/2023] [Revised: 06/25/2023] [Accepted: 06/28/2023] [Indexed: 01/13/2024] Open
Abstract
The primary aim of this research was to investigate potential differences of breast tumor morphologies across African American and Caucasian racial groups by utilizing machine learning (ML) and artificial intelligence (AI) methods. While breast cancer disparities can partially be attributed to social determinants of health, tumor biology also contributes to survival outcomes. The rate of breast tumor growth is largely dependent on the extracellular matrix (ECM). Current research suggests the cellular components of the ECM may vary among racial and ethnic populations, and this may contribute to the incidence of cancer in African Americans. We utilized a supervised AI method to evaluate morphological differences between African American and Caucasian breast cancer tumors. Images used for analysis were downloaded from the Cancer Genome Atlas (TCGA) biorepository stored in the NIH Genomic Data Commons (GDC) data portal. We designed an ML classifier using the AlexNet model provided in PyTorch's torchvision package. The model was pre-trained and adapted via transfer learning resulting in a classification accuracy of 92.1% when using our breast cancer tumor image database split into 80% of training set and 20% of testing set. We interpreted the results of the AlexNet and ResNet50 models using LIME and saliency mapping as the explainers. Based on the images from our bi-racial testing set, this study confirmed significant variations of tumor and ECM regions in the different racial groups evaluated. Based on these findings, further analysis and characterization may provide new insight into disparities associated with the incidence of breast cancer.
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Affiliation(s)
- A. Stone
- Dept. of Animal & Veterinary Sciences, Clemson University, Clemson, SC, USA
| | - C. Kalahiki
- School of Computing, Clemson University, Clemson, SC, USA
| | - L. Li
- School of Computing, Clemson University, Clemson, SC, USA
| | - N. Hubig
- School of Computing, Clemson University, Clemson, SC, USA
| | - F. Iuricich
- School of Computing, Clemson University, Clemson, SC, USA
| | - H. Dunn
- Dept. of Bioengineering, Clemson University, Clemson, SC, USA
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29
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Gouel P, Callonnec F, Levêque É, Valet C, Blôt A, Cuvelier C, Saï S, Saunier L, Pepin LF, Hapdey S, Libraire J, Vera P, Viard B. Evaluation of the capability and reproducibility of RECIST 1.1. measurements by technologists in breast cancer follow-up: a pilot study. Sci Rep 2023; 13:9148. [PMID: 37277412 DOI: 10.1038/s41598-023-36315-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023] Open
Abstract
The evaluation of tumor follow-up according to RECIST 1.1 has become essential in clinical practice given its role in therapeutic decision making. At the same time, radiologists are facing an increase in activity while facing a shortage. Radiographic technologists could contribute to the follow-up of these measures, but no studies have evaluated their ability to perform them. Ninety breast cancer patients were performed three CT follow-ups between September 2017 and August 2021. 270 follow-up treatment CT scans were analyzed including 445 target lesions. The rate of agreement of classifications RECIST 1.1 between five technologists and radiologists yielded moderate (k value between 0.47 and 0.52) and substantial (k value = 0.62 and k = 0.67) agreement values. 112 CT were classified as progressive disease (PD) by the radiologists, and 414 new lesions were identified. The analysis showed a percentage of strict agreement of progressive disease classification between reader-technologists and radiologists ranging from substantial to almost perfect agreement (range 73-97%). Analysis of intra-observer agreement was strong at almost perfect (k > 0.78) for 3 technologists. These results are encouraging regarding the ability of selected technologists to perform measurements according to RECIST 1.1 criteria by CT scan with good identification of disease progression.
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Affiliation(s)
- Pierrick Gouel
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France.
- QuantIF-LITIS EA4108, University of Rouen, Rouen, Normandy, France.
| | - Françoise Callonnec
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Émilie Levêque
- Department of Statistics and Clinical Research Unit, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Céline Valet
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Axelle Blôt
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Clémence Cuvelier
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Sonia Saï
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Lucie Saunier
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Louis-Ferdinand Pepin
- Department of Statistics and Clinical Research Unit, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Sébastien Hapdey
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
- QuantIF-LITIS EA4108, University of Rouen, Rouen, Normandy, France
| | - Julie Libraire
- Department of Statistics and Clinical Research Unit, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Pierre Vera
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
- QuantIF-LITIS EA4108, University of Rouen, Rouen, Normandy, France
| | - Benjamin Viard
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
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30
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Chen Y, Li H, Janowczyk A, Toro P, Corredor G, Whitney J, Lu C, Koyuncu CF, Mokhtari M, Buzzy C, Ganesan S, Feldman MD, Fu P, Corbin H, Harbhajanka A, Gilmore H, Goldstein LJ, Davidson NE, Desai S, Parmar V, Madabhushi A. Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer. NPJ Breast Cancer 2023; 9:40. [PMID: 37198173 PMCID: PMC10192429 DOI: 10.1038/s41523-023-00545-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN-) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN- IBC. H&E images from a total of n = 321 patients with ER+ and LN- IBC from three cohorts were employed for this study (Training set: D1 (n = 116), Validation sets: D2 (n = 121) and D3 (n = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02-5.32, p = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18-7.35, p = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20-89.18, p = 0.0106; D1: p = 0.0238; D2: p = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.
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Grants
- R01 CA216579 NCI NIH HHS
- C06 RR012463 NCRR NIH HHS
- R43 EB028736 NIBIB NIH HHS
- U01 CA239055 NCI NIH HHS
- R01 CA249992 NCI NIH HHS
- UG1 CA233328 NCI NIH HHS
- R01 CA220581 NCI NIH HHS
- R01 CA202752 NCI NIH HHS
- R01 CA208236 NCI NIH HHS
- U01 CA248226 NCI NIH HHS
- I01 BX004121 BLRD VA
- R01 CA257612 NCI NIH HHS
- U54 CA254566 NCI NIH HHS
- Research reported in this study was supported by the National Cancer Institute under award numbers R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01, National Heart, Lung and Blood Institute 1R01HL15127701A1, R01HL15807101A1, National Institute of Biomedical Imaging and Bioengineering 1R43EB028736-01, National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345, W81XWH-21-1-0160), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly and Astrazeneca.
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Affiliation(s)
- Yuli Chen
- Shaanxi Normal University, School of Computer Science, Xi'an, China
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Haojia Li
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Precision Oncology Center, University of Lausanne, Lausanne, Switzerland
| | - Paula Toro
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Germán Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Cheng Lu
- Shaanxi Normal University, School of Computer Science, Xi'an, China
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Can F Koyuncu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mojgan Mokhtari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Christina Buzzy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Michael D Feldman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, USA
| | - Haley Corbin
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Hannah Gilmore
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Nancy E Davidson
- Fred Hutchinson Cancer Research Center, University of Washington, and Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Sangeeta Desai
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Vani Parmar
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Anant Madabhushi
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Louis Stokes VA Medical Center, Cleveland, OH, USA.
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Chopra S, Khosla M, Vidya R. Innovations and Challenges in Breast Cancer Care: A Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050957. [PMID: 37241189 DOI: 10.3390/medicina59050957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023]
Abstract
Breast cancer care has seen tremendous advancements in recent years through various innovations to improve early detection, diagnosis, treatment, and survival. These innovations include advancements in imaging techniques, minimally invasive surgical techniques, targeted therapies and personalized medicine, radiation therapy, and multidisciplinary care. It is essential to recognize that challenges and limitations exist while significant advancements in breast cancer care exist. Continued research, advocacy, and efforts to address these challenges are necessary to make these innovations accessible to all patients while carefully considering and managing the ethical, social, and practical implications.
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Affiliation(s)
- Sharat Chopra
- Aneurin Bevan University Health Board, The Royal Gwent Hospital, Newport NP20 2UB, UK
| | - Muskaan Khosla
- The Royal Wolverhampton NHS Trust, Wolverhampton WV10 0QP, UK
| | - Raghavan Vidya
- The Royal Wolverhampton NHS Trust, Wolverhampton WV10 0QP, UK
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32
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Odhiambo P, Okello H, Wakaanya A, Wekesa C, Okoth P. Mutational signatures for breast cancer diagnosis using artificial intelligence. J Egypt Natl Canc Inst 2023; 35:14. [PMID: 37184779 DOI: 10.1186/s43046-023-00173-4] [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: 07/02/2022] [Accepted: 04/19/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Breast cancer is the most common female cancer worldwide. Its diagnosis and prognosis remain scanty, imprecise, and poorly documented. Previous studies have indicated that some genetic mutational signatures are suspected to lead to progression of various breast cancer scenarios. There is paucity of data on the role of AI tools in delineating breast cancer mutational signatures. This study sought to investigate the relationship between breast cancer genetic mutational profiles using artificial intelligence models with a view to developing an accurate prognostic prediction based on breast cancer genetic signatures. Prior research on breast cancer has been based on symptoms, origin, and tumor size. It has not been investigated whether diagnosis of breast cancer can be made utilizing AI platforms like Cytoscape, Phenolyzer, and Geneshot with potential for better prognostic power. This is the first ever attempt for a combinatorial approach to breast cancer diagnosis using different AI platforms. METHOD Artificial intelligence (AI) are mathematical algorithms that simulate human cognitive abilities and solve difficult healthcare issues such as complicated biological abnormalities like those experienced in breast cancer scenarios. The current models aimed to predict outcomes and prognosis by correlating imaging phenotypes with genetic mutations, tumor profiles, and hormone receptor status and development of imaging biomarkers that combine tumor and patient-specific features. Geneshotsav 2021, Cytoscape 3.9.1, and Phenolyzer Nature Methods, 12:841-843 (2015) tools, were used to mine breast cancer-associated mutational signatures and provided useful alternative computational tools for discerning pathways and enriched networks of genes of similarity with the overall goal of providing a systematic view of the variety of mutational processes that lead to breast cancer development. The development of novel-tailored pharmaceuticals, as well as the distribution of prospective treatment alternatives, would be aided by the collection of massive datasets and the use of such tools as diagnostic markers. RESULTS Specific DNA-maintenance defects, endogenous or environmental exposures, and cancer genomic signatures are connected. The PubMed database (Geneshot) search for the keywords yielded a total of 21,921 genes associated with breast cancer. Then, based on their propensity to result in gene mutations, the genes were screened using the Phenolyzer software. These platforms lend credence to the fact that breast cancer diagnosis using Cytoscape 3.9.1, Phenolyzer, and Geneshot 2021 reveals high profile of the following mutational signatures: BRCA1, BRCA2, TP53, CHEK2, PTEN, CDH1, BRIP1, RAD51C, CASP3, CREBBP, and SMAD3.
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Affiliation(s)
- Patrick Odhiambo
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya.
| | - Harrison Okello
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Annette Wakaanya
- Department of Mathematics, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Clabe Wekesa
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Patrick Okoth
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
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Hacking SM, Karam J, Singh K, Gamsiz Uzun ED, Brickman A, Yakirevich E, Taliano R, Wang Y. Whole slide image features predict pathologic complete response and poor clinical outcomes in triple-negative breast cancer. Pathol Res Pract 2023; 246:154476. [PMID: 37146413 DOI: 10.1016/j.prp.2023.154476] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/16/2023] [Accepted: 04/18/2023] [Indexed: 05/07/2023]
Abstract
INTRODUCTION Breast cancers are complex ecosystem like networks of malignant cells and their associated microenvironment. Applications for machine intelligence and the tumoral microenvironment are expanding frontiers in pathology. Previously, computational approaches have been developed to quantify and spatially analyze immune cells, proportionate stroma, and detect tumor budding. Little work has been done to analyze different types of tumor-associated stromata both quantitatively and computationally in relation to clinical endpoints. METHODS We aimed to quantify stromal features from whole slide images (WSI) including stromata (myxoid, collagenous, immune) and tumoral components and combined them with traditional clinical and pathologic parameters in 120 triple-negative breast cancer (TNBC) patients treated with neoadjuvant chemotherapy (NAC) to predict pathologic complete response (pCR) and poor clinical outcomes. RESULTS High collagenous stroma on WSI was best associated with lower rates of pCR, while combined high proportionated stroma (myxoid, collagenous, and immune) most optimally predicted worse clinical survival outcomes. When combining clinical, pathologic, and WSI features, Receiver Operator Characteristics (ROC) curves for LASSO features was up to 0.67 for pCR and 0.77 for poor outcomes. CONCLUSION The techniques demonstrated in the present study can be performed with appropriate quality assurance. Future trials are needed to demonstrate whether coupling applications for machine intelligence, inclusive of the tumor mesenchyme, can improve outcomes prediction for patients with breast cancer.
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Affiliation(s)
- Sean M Hacking
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Julie Karam
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
| | - Kamaljeet Singh
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Women and Infants Hospital, Providence, RI, United States
| | - Ece D Gamsiz Uzun
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States; Center for Computational Molecular Biology, Brown University, Providence, RI, United States
| | - Arlen Brickman
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Evgeny Yakirevich
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Ross Taliano
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Yihong Wang
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI, United States; Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States.
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34
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Baron JM. Artificial Intelligence in the Clinical Laboratory: An Overview with Frequently Asked Questions. Clin Lab Med 2023; 43:1-16. [PMID: 36764803 DOI: 10.1016/j.cll.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This article provides an overview of machine learning fundamentals and some applications of machine learning to clinical laboratory diagnostics and patient management. A key goal of this article is to provide a basic foundation in clinical machine learning for readers with clinical laboratory experience that will set them up for more in-depth study of the topic and/or to become a better collaborator with computational colleagues in the development and deployment of machine learning-based solutions.
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Affiliation(s)
- Jason M Baron
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114-2696, USA.
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35
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Greenberg A, Samueli B, Fahoum I, Farkash S, Greenberg O, Zemser-Werner V, Sabo E, Hagege RR, Hershkovitz D. Short Training Significantly Improves Ganglion Cell Detection Using an Algorithm-Assisted Approach. Arch Pathol Lab Med 2023; 147:215-221. [PMID: 35738006 DOI: 10.5858/arpa.2021-0481-oa] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2022] [Indexed: 02/05/2023]
Abstract
CONTEXT.— Medical education in pathology relies on the accumulation of experience gained through inspection of numerous samples from each entity. Acquiring sufficient teaching material for rare diseases, such as Hirschsprung disease (HSCR), may be difficult, especially in smaller institutes. The current study makes use of a previously developed decision support system using a decision support algorithm meant to aid pathologists in the diagnosis of HSCR. OBJECTIVE.— To assess the effect of a short training session on algorithm-assisted HSCR diagnosis. DESIGN.— Five pathologists reviewed a data set of 568 image sets (1704 images in total) selected from 50 cases by the decision support algorithm and were tasked with scoring the images for the presence or absence of ganglion cells. The task was repeated a total of 3 times. Each pathologist had to complete a short educational presentation between the second and third iterations. RESULTS.— The training resulted in a significantly increased rate of correct diagnoses (true positive/negative) and a decreased need for referrals for expert consultation. No statistically significant changes in the rate of false positives/negatives were detected. CONCLUSIONS.— A very short (<10 minutes) training session can greatly improve the pathologist's performance in the algorithm-assisted diagnosis of HSCR. The same approach may be feasible in training for the diagnosis of other rare diseases.
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Affiliation(s)
- Ariel Greenberg
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Benzion Samueli
- From the Department of Pathology, Soroka University Medical Center, Be'er Sheva, Israel (Samueli)
| | - Ibrahim Fahoum
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Shai Farkash
- From the Institute of Pathology, Emek Medical Center, Afula, Israel (Farkash)
| | - Orli Greenberg
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Valentina Zemser-Werner
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz)
| | - Edmond Sabo
- From the Institute of Pathology, Carmel Medical Center, Haifa, Israel (Sabo).,From the Rappaport Faculty of Medicine, Technion, Haifa, Israel (Sabo)
| | - Rami R Hagege
- From Institute of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel (A Greenberg, Fahoum, O Greenberg, Zemser-Werner, Hagege, Hershkovitz).,Hagege and Hershkovitz contributed equally to the research
| | - Dov Hershkovitz
- From the Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel (Hershkovitz).,Hagege and Hershkovitz contributed equally to the research
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36
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An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image. J Imaging 2023; 9:jimaging9010012. [PMID: 36662110 PMCID: PMC9866917 DOI: 10.3390/jimaging9010012] [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: 11/27/2022] [Revised: 12/30/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023] Open
Abstract
The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype "Luminal A". It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis.
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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Chen J, Yang Y, Luo B, Wen Y, Chen Q, Ma R, Huang Z, Zhu H, Li Y, Chen Y, Qian D. Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer. Hum Pathol 2023; 131:26-37. [PMID: 36481204 DOI: 10.1016/j.humpath.2022.11.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/22/2022] [Accepted: 11/27/2022] [Indexed: 12/12/2022]
Abstract
Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P < 0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P = 0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P = 0.002), and solidity (0.983 vs. 0.934, P = 0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.
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Affiliation(s)
- Jiamei Chen
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yang Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China
| | - Bo Luo
- Department of Pathology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yaofeng Wen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China
| | - Qingzhong Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China
| | - Ru Ma
- Department of Peritoneal Cancer Surgery, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, China
| | - Zhen Huang
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Hangjia Zhu
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yan Li
- Department of Peritoneal Cancer Surgery, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, China; Department of Pathology, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, China.
| | - Yongshun Chen
- Center of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200000, China.
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A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010003. [PMID: 36675952 PMCID: PMC9864148 DOI: 10.3390/life13010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/09/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
We aimed to develop an artificial intelligence (AI) diagnosis system for uterine smooth muscle tumors (UMTs) by using deep learning. We analyzed the morphological features of UMTs on whole-slide images (233, 108, and 30 digital slides of leiomyosarcomas, leiomyomas, and smooth muscle tumors of uncertain malignant potential stained with hematoxylin and eosin, respectively). Aperio ImageScope software randomly selected ≥10 areas of the total field of view. Pathologists randomly selected a marked region in each section that was no smaller than the total area of 10 high-power fields in which necrotic, vascular, collagenous, and mitotic areas were labeled. We constructed an automatic identification algorithm for cytological atypia and necrosis by using ResNet and constructed an automatic detection algorithm for mitosis by using YOLOv5. A logical evaluation algorithm was then designed to obtain an automatic UMT diagnostic aid that can "study and synthesize" a pathologist's experience. The precision, recall, and F1 index reached more than 0.920. The detection network could accurately detect the mitoses (0.913 precision, 0.893 recall). For the prediction ability, the AI system had a precision of 0.90. An AI-assisted system for diagnosing UMTs in routine practice scenarios is feasible and can improve the accuracy and efficiency of diagnosis.
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Al Rimon R, Nelson VL, Brunt KR, Kassiri Z. High-impact opportunities to address ischemia: a focus on heart and circulatory research. Am J Physiol Heart Circ Physiol 2022; 323:H1221-H1230. [PMID: 36331554 DOI: 10.1152/ajpheart.00402.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Myocardial ischemic injury and its resolution are the key determinants of morbidity or mortality in heart failure. The cause and duration of ischemia in patients vary. Numerous experimental models and methods have been developed to define genetic, metabolic, molecular, cellular, and pathophysiological mechanisms, in addition to defining structural and functional deterioration of cardiovascular performance. The rapid rise of big data, such as single-cell analysis techniques with bioinformatics, machine learning, and neural networking, brings a new level of sophistication to our understanding of myocardial ischemia. This mini-review explores the multifaceted nature of ischemic injury in the myocardium. We highlight recent state-of-the-art findings and strategies to show new directions of high-impact approach to understanding myocardial tissue remodeling. This next age of heart and circulatory physiology research will be more comprehensive and collaborative to uncover the origin, progression, and manifestation of heart failure while strengthening novel treatment strategies.
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Affiliation(s)
- Razoan Al Rimon
- Department of Physiology, Cardiovascular Research Center, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Victoria L Nelson
- Department of Pharmacology, Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Keith R Brunt
- Department of Pharmacology, Faculty of Medicine, Dalhousie University, Saint John, New Brunswick, Canada
| | - Zamaneh Kassiri
- Department of Physiology, Cardiovascular Research Center, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
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Kiddle A, Barham H, Wegerif S, Petronzio C. Dynamic region of interest selection in remote photoplethysmography: proof of principle (Preprint). JMIR Form Res 2022; 7:e44575. [PMID: 36995742 PMCID: PMC10131655 DOI: 10.2196/44575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Remote photoplethysmography (rPPG) can record vital signs (VSs) by detecting subtle changes in the light reflected from the skin. Lifelight (Xim Ltd) is a novel software being developed as a medical device for the contactless measurement of VSs using rPPG via integral cameras on smart devices. Research to date has focused on extracting the pulsatile VS from the raw signal, which can be influenced by factors such as ambient light, skin thickness, facial movements, and skin tone. OBJECTIVE This preliminary proof-of-concept study outlines a dynamic approach to rPPG signal processing wherein green channel signals from the most relevant areas of the face (the midface, comprising the cheeks, nose, and top of the lip) are optimized for each subject using tiling and aggregation (T&A) algorithms. METHODS High-resolution 60-second videos were recorded during the VISION-MD study. The midface was divided into 62 tiles of 20×20 pixels, and the signals from multiple tiles were evaluated using bespoke algorithms through weighting according to signal-to-noise ratio in the frequency domain (SNR-F) score or segmentation. Midface signals before and after T&A were categorized by a trained observer blinded to the data processing as 0 (high quality, suitable for algorithm training), 1 (suitable for algorithm testing), or 2 (inadequate quality). On secondary analysis, observer categories were compared for signals predicted to improve categories following T&A based on the SNR-F score. Observer ratings and SNR-F scores were also compared before and after T&A for Fitzpatrick skin tones 5 and 6, wherein rPPG is hampered by light absorption by melanin. RESULTS The analysis used 4310 videos recorded from 1315 participants. Category 2 and 1 signals had lower mean SNR-F scores than category 0 signals. T&A improved the mean SNR-F score using all algorithms. Depending on the algorithm, 18% (763/4212) to 31% (1306/4212) of signals improved by at least one category, with up to 10% (438/4212) improving into category 0, and 67% (2834/4212) to 79% (3337/4212) remaining in the same category. Importantly, 9% (396/4212) to 21% (875/4212) improved from category 2 (not usable) into category 1. All algorithms showed improvements. No more than 3% (137/4212) of signals were assigned to a lower-quality category following T&A. On secondary analysis, 62% of signals (32/52) were recategorized, as predicted from the SNR-F score. T&A improved SNR-F scores in darker skin tones; 41% of signals (151/369) improved from category 2 to 1 and 12% (44/369) from category 1 to 0. CONCLUSIONS The T&A approach to dynamic region of interest selection improved signal quality, including in dark skin tones. The method was verified by comparison with a trained observer's rating. T&A could overcome factors that compromise whole-face rPPG. This method's performance in estimating VS is currently being assessed. TRIAL REGISTRATION ClinicalTrials.gov NCT04763746; https://clinicaltrials.gov/ct2/show/NCT04763746.
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Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging 2022; 22:187. [PMID: 36316665 PMCID: PMC9620604 DOI: 10.1186/s12880-022-00918-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/22/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.
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Affiliation(s)
- Iben Fasterholdt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mohammad Naghavi-Behzad
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Benjamin S. B. Rasmussen
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Radiology, Odense University Hospital, Odense, Denmark ,grid.7143.10000 0004 0512 5013CAI-X – Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
| | - Tue Kjølhede
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mette Maria Skjøth
- grid.7143.10000 0004 0512 5013Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Malene Grubbe Hildebrandt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark ,grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Kristian Kidholm
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
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Deep learning models for histologic grading of breast cancer and association with disease prognosis. NPJ Breast Cancer 2022; 8:113. [PMID: 36192400 PMCID: PMC9530224 DOI: 10.1038/s41523-022-00478-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 09/01/2022] [Indexed: 12/02/2022] Open
Abstract
Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.
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Liu Y, Bilodeau E, Pollack B, Batmanghelich K. Automated detection of premalignant oral lesions on whole slide images using convolutional neural networks. Oral Oncol 2022; 134:106109. [PMID: 36126604 DOI: 10.1016/j.oraloncology.2022.106109] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/12/2022] [Accepted: 08/29/2022] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Oral epithelial dysplasia (OED) is a precursor lesion to oral squamous cell carcinoma, a disease with a reported overall survival rate of 56 percent across all stages. Accurate detection of OED is critical as progression to oral cancer can be impeded with complete excision of premalignant lesions. However, previous research has demonstrated that the task of grading of OED, even when performed by highly trained experts, is subject to high rates of reader variability and misdiagnosis. Thus, our study aims to develop a convolutional neural network (CNN) model that can identify regions suspicious for OED whole-slide pathology images. METHODS During model development, we optimized key training hyperparameters including loss function on 112 pathologist annotated cases between the training and validation sets. Then, we compared OED segmentation and classification metrics between two well-established CNN architectures for medical imaging, DeepLabv3+ and UNet++. To further assess generalizability, we assessed case-level performance of a held-out test set of 44 whole-slide images. RESULTS DeepLabv3+ outperformed UNet++ in overall accuracy, precision, and segmentation metrics in a 4-fold cross validation study. When applied to the held-out test set, our best performing DeepLabv3+ model achieved an overall accuracy and F1-Score of 93.3 percent and 90.9 percent, respectively. CONCLUSION The present study trained and implemented a CNN-based deep learning model for identification and segmentation of oral epithelial dysplasia (OED) with reasonable success. Computer assisted detection was shown to be feasible in detecting premalignant/precancerous oral lesions, laying groundwork for eventual clinical implementation.
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Affiliation(s)
- Yingci Liu
- University of Pittsburgh, Department of Biomedical Informatics, 5607, Baum Boulevard, Pittsburgh, PA 15206, USA; Rutgers School of Dental Medicine, 110, Bergen St, Newark, NJ 07101, USA.
| | - Elizabeth Bilodeau
- University of Pittsburgh School of Dental Medicine, 3501 Terrace St., Pittsburgh, PA 15206, USA
| | - Brian Pollack
- University of Pittsburgh, Department of Biomedical Informatics, 5607, Baum Boulevard, Pittsburgh, PA 15206, USA
| | - Kayhan Batmanghelich
- University of Pittsburgh, Department of Biomedical Informatics, 5607, Baum Boulevard, Pittsburgh, PA 15206, USA
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Hameed Z, Garcia-Zapirain B, Aguirre JJ, Isaza-Ruget MA. Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network. Sci Rep 2022; 12:15600. [PMID: 36114214 PMCID: PMC9649689 DOI: 10.1038/s41598-022-19278-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 08/26/2022] [Indexed: 12/03/2022] Open
Abstract
Breast cancer is a common malignancy and a leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity and mortality rates in women. To this end, histopathological diagnosis is usually followed as the gold standard approach. However, this process is tedious, labor-intensive, and may be subject to inter-reader variability. Accordingly, an automatic diagnostic system can assist to improve the quality of diagnosis. This paper presents a deep learning approach to automatically classify hematoxylin-eosin-stained breast cancer microscopy images into normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma using our collected dataset. Our proposed model exploited six intermediate layers of the Xception (Extreme Inception) network to retrieve robust and abstract features from input images. First, we optimized the proposed model on the original (unnormalized) dataset using 5-fold cross-validation. Then, we investigated its performance on four normalized datasets resulting from Reinhard, Ruifrok, Macenko, and Vahadane stain normalization. For original images, our proposed framework yielded an accuracy of 98% along with a kappa score of 0.969. Also, it achieved an average AUC-ROC score of 0.998 as well as a mean AUC-PR value of 0.995. Specifically, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. For normalized images, the proposed architecture performed better for Makenko normalization compared to the other three techniques. In this case, the proposed model achieved an accuracy of 97.79% together with a kappa score of 0.965. Also, it attained an average AUC-ROC score of 0.997 and a mean AUC-PR value of 0.991. Especially, for in situ carcinoma and invasive carcinoma, it offered sensitivity of 96% and 99%, respectively. These results demonstrate that our proposed model outperformed the baseline AlexNet as well as state-of-the-art VGG16, VGG19, Inception-v3, and Xception models with their default settings. Furthermore, it can be inferred that although stain normalization techniques offered competitive performance, they could not surpass the results of the original dataset.
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Thakur N, Alam MR, Abdul-Ghafar J, Chong Y. Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review. Cancers (Basel) 2022; 14:cancers14143529. [PMID: 35884593 PMCID: PMC9316753 DOI: 10.3390/cancers14143529] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 11/27/2022] Open
Abstract
Simple Summary Artificial intelligence (AI) has attracted significant interest in the healthcare sector due to its promising results. Cytological examination is a critical step in the initial diagnosis of cancer. Here, we conducted a systematic review with quantitative analysis to understand the current status of AI applications in non-gynecological (non-GYN) cancer cytology. In our analysis, we found that most of the studies focused on classification and segmentation tasks. Overall, AI showed promising results for non-GYN cancer cytopathology analysis. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation across all studies. Abstract State-of-the-art artificial intelligence (AI) has recently gained considerable interest in the healthcare sector and has provided solutions to problems through automated diagnosis. Cytological examination is a crucial step in the initial diagnosis of cancer, although it shows limited diagnostic efficacy. Recently, AI applications in the processing of cytopathological images have shown promising results despite the elementary level of the technology. Here, we performed a systematic review with a quantitative analysis of recent AI applications in non-gynecological (non-GYN) cancer cytology to understand the current technical status. We searched the major online databases, including MEDLINE, Cochrane Library, and EMBASE, for relevant English articles published from January 2010 to January 2021. The searched query terms were: “artificial intelligence”, “image processing”, “deep learning”, “cytopathology”, and “fine-needle aspiration cytology.” Out of 17,000 studies, only 26 studies (26 models) were included in the full-text review, whereas 13 studies were included for quantitative analysis. There were eight classes of AI models treated of according to target organs: thyroid (n = 11, 39%), urinary bladder (n = 6, 21%), lung (n = 4, 14%), breast (n = 2, 7%), pleural effusion (n = 2, 7%), ovary (n = 1, 4%), pancreas (n = 1, 4%), and prostate (n = 1, 4). Most of the studies focused on classification and segmentation tasks. Although most of the studies showed impressive results, the sizes of the training and validation datasets were limited. Overall, AI is also promising for non-GYN cancer cytopathology analysis, such as pathology or gynecological cytology. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation found across all studies. Future studies with larger datasets with high-quality annotations and external validation are required.
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Hacking SM, Yakirevich E, Wang Y. From Immunohistochemistry to New Digital Ecosystems: A State-of-the-Art Biomarker Review for Precision Breast Cancer Medicine. Cancers (Basel) 2022; 14:cancers14143469. [PMID: 35884530 PMCID: PMC9315712 DOI: 10.3390/cancers14143469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary In this state-of-the-art breast biomarker review, we have tried to imagine and illustrate future, emerging digital breast cancer ecosystems which allow for greater incorporation of traditional immunohistochemical and molecular biomarkers, WSI, and radiomic features. Abstract Breast cancers represent complex ecosystem-like networks of malignant cells and their associated microenvironment. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are biomarkers ubiquitous to clinical practice in evaluating prognosis and predicting response to therapy. Recent feats in breast cancer have led to a new digital era, and advanced clinical trials have resulted in a growing number of personalized therapies with corresponding biomarkers. In this state-of-the-art review, we included the latest 10-year updated recommendations for ER, PR, and HER2, along with the most salient information on tumor-infiltrating lymphocytes (TILs), Ki-67, PD-L1, and several prognostic/predictive biomarkers at genomic, transcriptomic, and proteomic levels recently developed for selection and optimization of breast cancer treatment. Looking forward, the multi-omic landscape of the tumor ecosystem could be integrated with computational findings from whole slide images and radiomics in predictive machine learning (ML) models. These are new digital ecosystems on the road to precision breast cancer medicine.
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Affiliation(s)
| | | | - Yihong Wang
- Correspondence: ; Tel.: +1-401-444-9897; Fax: +1-401-444-4377
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 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)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022; 159:333-346. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/11/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
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Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022; 39:120. [PMID: 35704152 PMCID: PMC9198206 DOI: 10.1007/s12032-022-01711-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/14/2022] [Indexed: 10/28/2022]
Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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