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Sanchez DF, Oliveira P. Pathology of Squamous Cell Carcinoma of the Penis: Back to Square One. Urol Clin North Am 2024; 51:313-325. [PMID: 38925734 DOI: 10.1016/j.ucl.2024.03.003] [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: 06/28/2024]
Abstract
The landscape of squamous cell carcinoma of the penis (SCC-P) has undergone a significant transformation since the new World Health Organization classification of genitourinary cancers and recent European Association of Urology/American Association of Clinical Oncology guidelines. These changes emphasize the necessity to categorize SCC-P into 2 groups based on its association with human papillomavirus (HPV) infection. This shift has major implications, considering that prior knowledge was derived from a mix of both groups. Given the distinct prognosis, treatment options, and staging systems observed for HPV-associated tumors in other body areas, the question now arises: will similar patterns emerge for SCC-P?
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Affiliation(s)
- Diego F Sanchez
- Translational Oncogenomics Group, Manchester Cancer Research Centre & CRUK-MI, Wilmslow Road, Manchester M20 4GJ, UK.
| | - Pedro Oliveira
- Department of Pathology, Christie NHS Foundation Trust, Wilmslow Road, Manchester M20 4BX, UK
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2
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Ma M, Zeng X, Qu L, Sheng X, Ren H, Chen W, Li B, You Q, Xiao L, Wang Y, Dai M, Zhang B, Lu C, Sheng W, Huang D. Advancing Automatic Gastritis Diagnosis: An Interpretable Multilabel Deep Learning Framework for the Simultaneous Assessment of Multiple Indicators. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:1538-1549. [PMID: 38762117 DOI: 10.1016/j.ajpath.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 03/17/2024] [Accepted: 04/26/2024] [Indexed: 05/20/2024]
Abstract
The evaluation of morphologic features, such as inflammation, gastric atrophy, and intestinal metaplasia, is crucial for diagnosing gastritis. However, artificial intelligence analysis for nontumor diseases like gastritis is limited. Previous deep learning models have omitted important morphologic indicators and cannot simultaneously diagnose gastritis indicators or provide interpretable labels. To address this, an attention-based multi-instance multilabel learning network (AMMNet) was developed to simultaneously achieve the multilabel diagnosis of activity, atrophy, and intestinal metaplasia with only slide-level weak labels. To evaluate AMMNet's real-world performance, a diagnostic test was designed to observe improvements in junior pathologists' diagnostic accuracy and efficiency with and without AMMNet assistance. In this study of 1096 patients from seven independent medical centers, AMMNet performed well in assessing activity [area under the curve (AUC), 0.93], atrophy (AUC, 0.97), and intestinal metaplasia (AUC, 0.93). The false-negative rates of these indicators were only 0.04, 0.08, and 0.18, respectively, and junior pathologists had lower false-negative rates with model assistance (0.15 versus 0.10). Furthermore, AMMNet reduced the time required per whole slide image from 5.46 to 2.85 minutes, enhancing diagnostic efficiency. In block-level clustering analysis, AMMNet effectively visualized task-related patches within whole slide images, improving interpretability. These findings highlight AMMNet's effectiveness in accurately evaluating gastritis morphologic indicators on multicenter data sets. Using multi-instance multilabel learning strategies to support routine diagnostic pathology deserves further evaluation.
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Affiliation(s)
- Mengke Ma
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China
| | - Xixi Zeng
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China
| | - Linhao Qu
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China
| | - Xia Sheng
- Department of Pathology, Minhang Hospital, Fudan University, Shanghai, China
| | - Hongzheng Ren
- Department of Pathology, Gongli Hospital, Naval Medical University, Shanghai, China
| | - Weixiang Chen
- Department of Pathology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bin Li
- Department of Pathology, Shanghai Xu-Hui Central Hospital, Shanghai, China
| | - Qinghua You
- Department of Pathology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China
| | - Li Xiao
- Department of Pathology, Huadong Hospital, Shanghai, China
| | - Yi Wang
- Information Center, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Mei Dai
- Information Center, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Boqiang Zhang
- Shanghai Foremost Medical Technology Co. Ltd., Shanghai, China
| | - Changqing Lu
- Shanghai Foremost Medical Technology Co. Ltd., Shanghai, China
| | - Weiqi Sheng
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China.
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China; Institute of Pathology, Fudan University, Shanghai, China.
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3
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Zhang DY, Venkat A, Khasawneh H, Sali R, Zhang V, Pei Z. Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice. J Transl Med 2024; 104:102111. [PMID: 39053633 DOI: 10.1016/j.labinv.2024.102111] [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: 12/29/2023] [Revised: 07/07/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024] Open
Abstract
The advent of affordable technology has significantly influenced the practice of digital pathology, leading to its growing adoption within the pathology community. This review article aimed to outline the latest developments in digital pathology, the cutting-edge advancements in artificial intelligence (AI) applications within this field, and the pertinent United States regulatory frameworks. The content is based on a thorough analysis of original research articles and official United States Federal guidelines. Findings from our review indicate that several Food and Drug Administration-approved digital scanners and image management systems are establishing a solid foundation for the seamless integration of advanced technologies into everyday pathology workflows, which may reduce device and operational costs in the future. AI is particularly transforming the way morphologic diagnoses are automated, notably in cancers like prostate and colorectal, within screening initiatives, albeit challenges such as data privacy issues and algorithmic biases remain. The regulatory environment, shaped by standards from the Food and Drug Administration, Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments, and College of American Pathologists, is evolving to accommodate these innovations while ensuring safety and reliability. Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments have issued policies to allow pathologists to review and render diagnoses using digital pathology remotely. Moreover, the introduction of new digital pathology Current Procedural Terminology codes designed to complement existing pathology Current Procedural Terminology codes is facilitating reimbursement processes. Overall, these advancements are heralding a new era in pathology that promises enhanced diagnostic precision and efficiency through digital and AI technologies, potentially improving patient care as well as bolstering educational and research activities.
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Affiliation(s)
- David Y Zhang
- Department of Computation, NovinoAI, Fort Lauderdale, Florida; Department of Veterans Affairs New York Harbor Healthcare System, New York, New York.
| | - Arsha Venkat
- School of Medicine, New York Medical College, New York, New York
| | - Hamdi Khasawneh
- King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan
| | - Rasoul Sali
- Department of Computation, NovinoAI, Fort Lauderdale, Florida; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Valerio Zhang
- Department of Computation, NovinoAI, Fort Lauderdale, Florida
| | - Zhiheng Pei
- Department of Veterans Affairs New York Harbor Healthcare System, New York, New York; Department of Pathology, New York University School of Medicine, New York, New York; Department of Medicine, New York University School of Medicine, New York, New York.
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4
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Rizk PA, Gonzalez MR, Galoaa BM, Girgis AG, Van Der Linden L, Chang CY, Lozano-Calderon SA. Machine Learning-Assisted Decision Making in Orthopaedic Oncology. JBJS Rev 2024; 12:01874474-202407000-00005. [PMID: 38991098 DOI: 10.2106/jbjs.rvw.24.00057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.
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Affiliation(s)
- Paul A Rizk
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marcos R Gonzalez
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bishoy M Galoaa
- Interdisciplinary Science & Engineering Complex (ISEC), Northeastern University, Boston, Massachusetts
| | - Andrew G Girgis
- Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Lotte Van Der Linden
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Connie Y Chang
- Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Santiago A Lozano-Calderon
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Bian C, Ashton G, Grant M, Rodriguez VP, Martin IP, Tsakiroglou AM, Cook M, Fergie M. Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach. Cancers (Basel) 2024; 16:2026. [PMID: 38893146 PMCID: PMC11171264 DOI: 10.3390/cancers16112026] [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: 04/19/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
In this study, the prognostic value of cellular morphology and spatial configurations in melanoma has been examined, aiming to complement traditional prognostic indicators like mitotic activity and tumor thickness. Through a computational pipeline using machine learning and deep learning methods, we quantified nuclei sizes within different spatial regions and analyzed their prognostic significance using univariate and multivariate Cox models. Nuclei sizes in the invasive band demonstrated a significant hazard ratio (HR) of 1.1 (95% CI: 1.03, 1.18). Similarly, the nuclei sizes of tumor cells and Ki67 S100 co-positive cells in the invasive band achieved HRs of 1.07 (95% CI: 1.02, 1.13) and 1.09 (95% CI: 1.04, 1.16), respectively. Our findings reveal that nuclei sizes, particularly in the invasive band, are potentially prognostic factors. Correlation analyses further demonstrated a meaningful relationship between cellular morphology and tumor progression, notably showing that nuclei size within the invasive band correlates substantially with tumor thickness. These results suggest the potential of integrating spatial and morphological analyses into melanoma prognostication.
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Affiliation(s)
- Chang Bian
- The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
| | - Garry Ashton
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Megan Grant
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Valeria Pavet Rodriguez
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Isabel Peset Martin
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Anna Maria Tsakiroglou
- The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
| | - Martin Cook
- Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
- Royal Surrey County Hospital, Guildford GU2 7XX, UK
| | - Martin Fergie
- The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK
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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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Prassas I, Clarke B, Youssef T, Phlamon J, Dimitrakopoulos L, Rofaeil A, Yousef GM. Computational pathology: an evolving concept. Clin Chem Lab Med 2024; 0:cclm-2023-1124. [PMID: 38646706 DOI: 10.1515/cclm-2023-1124] [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: 10/24/2023] [Accepted: 04/10/2024] [Indexed: 04/23/2024]
Abstract
The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.
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Affiliation(s)
- Ioannis Prassas
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Blaise Clarke
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Timothy Youssef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - Juliana Phlamon
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | | | - Andrew Rofaeil
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
| | - George M Yousef
- Laboratory Medicine Program, 7989 University Health Network , Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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Hu S, Xiao Q, Gao R, Qin J, Nie J, Chen Y, Lou J, Ding M, Pan Y, Wang S. Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning. BMC Cancer 2024; 24:516. [PMID: 38654221 PMCID: PMC11041013 DOI: 10.1186/s12885-024-12251-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/11/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC. METHODS The data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models. RESULTS Our study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis. CONCLUSION In summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.
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Affiliation(s)
- Shangshang Hu
- School of Medicine, Southeast University, 210009, Nanjing, Jiangsu, China
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, 210006, Nanjing, Jiangsu, China
| | - Qianni Xiao
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 211122, Nanjing, Jiangsu, China
| | - Rui Gao
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 211122, Nanjing, Jiangsu, China
| | - Jian Qin
- School of Medicine, Southeast University, 210009, Nanjing, Jiangsu, China
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, 210006, Nanjing, Jiangsu, China
| | - Junjie Nie
- School of Medicine, Southeast University, 210009, Nanjing, Jiangsu, China
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, 210006, Nanjing, Jiangsu, China
| | - Yuhan Chen
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 211122, Nanjing, Jiangsu, China
| | - Jinwei Lou
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 211122, Nanjing, Jiangsu, China
| | - Muzi Ding
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 211122, Nanjing, Jiangsu, China
| | - Yuqin Pan
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, 210006, Nanjing, Jiangsu, China.
- Jiangsu Collaborative Innovation Center on Cancer Personalized Medicine, Nanjing Medical University, 211100, Nanjing, Jiangsu, China.
| | - Shukui Wang
- School of Medicine, Southeast University, 210009, Nanjing, Jiangsu, China.
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, 210006, Nanjing, Jiangsu, China.
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 211122, Nanjing, Jiangsu, China.
- Jiangsu Collaborative Innovation Center on Cancer Personalized Medicine, Nanjing Medical University, 211100, Nanjing, Jiangsu, China.
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Cai X, Zhang H, Wang Y, Zhang J, Li T. Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts. Int J Oral Sci 2024; 16:16. [PMID: 38403665 PMCID: PMC10894880 DOI: 10.1038/s41368-024-00287-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 12/24/2023] [Accepted: 02/01/2024] [Indexed: 02/27/2024] Open
Abstract
Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.
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Affiliation(s)
- Xinjia Cai
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China
| | - Heyu Zhang
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, China
| | - Yanjin Wang
- Department of Oral Pathology, Shanghai Stomatological Hospital & School of Stomatology, Fudan University, Shanghai, China
| | - Jianyun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China.
| | - Tiejun Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, China.
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10
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Brancato V, Esposito G, Coppola L, Cavaliere C, Mirabelli P, Scapicchio C, Borgheresi R, Neri E, Salvatore M, Aiello M. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med 2024; 22:136. [PMID: 38317237 PMCID: PMC10845786 DOI: 10.1186/s12967-024-04891-8] [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: 11/28/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.
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Affiliation(s)
| | - Giuseppina Esposito
- Bio Check Up S.R.L, 80121, Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131, Naples, Italy
| | | | | | - Peppino Mirabelli
- UOS Laboratori di Ricerca e Biobanca, AORN Santobono-Pausilipon, Via Teresa Ravaschieri, 8, 80122, Naples, Italy
| | - Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, via Roma, 67, 56126, Pisa, Italy
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11
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Jones JL, Poulsom R, Coates PJ. Recent Advances in Pathology: the 2023 Annual Review Issue of The Journal of Pathology. J Pathol 2023; 260:495-497. [PMID: 37580852 DOI: 10.1002/path.6192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 07/21/2023] [Indexed: 08/16/2023]
Abstract
The 2023 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 12 invited reviews on topics of current interest in pathology. This year, our subjects include immuno-oncology and computational pathology approaches for diagnostic and research applications in human disease. Reviews on the tissue microenvironment include the effects of apoptotic cell-derived exosomes, how understanding the tumour microenvironment predicts prognosis, and the growing appreciation of the diverse functions of fibroblast subtypes in health and disease. We also include up-to-date reviews of modern aspects of the molecular basis of malignancies, and our final review covers new knowledge of vascular and lymphatic regeneration in cardiac disease. All of the reviews contained in this issue are written by expert groups of authors selected to discuss the recent progress in their particular fields and all articles are freely available online (https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896). © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- J Louise Jones
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Richard Poulsom
- The Pathological Society of Great Britain and Ireland, London, UK
| | - Philip J Coates
- Research Center for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
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