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Xu Y, Huang W, Duan H, Xiao F. Bimetal-organic framework-integrated electrochemical sensor for on-chip detection of H 2S and H 2O 2 in cancer tissues. Biosens Bioelectron 2024; 260:116463. [PMID: 38838574 DOI: 10.1016/j.bios.2024.116463] [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: 04/10/2024] [Revised: 05/16/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
Studies on the interaction between hydrogen sulfide (H2S) and hydrogen peroxide (H2O2) in redox signaling motivate the development of a sensitive sensing platform for their discriminatory and dynamic detection. Herein, we present a fully integrated microfluidic on-chip electrochemical sensor for the online and simultaneous monitoring of H2S and H2O2 secreted by different biological samples. The sensor utilizes a cicada-wing-like RuCu bimetal-organic framework with uniform nanorods architecture that grows on a flexible carbon fiber microelectrode. Owing to the optimized electronic structural merits and satisfactory electrocatalytic properties, the resultant microelectrode shows remarkable electrochemical sensing performance for sensitive and selective detection of H2S and H2O2 at the same time. The result exhibits low detection limits of 0.5 μM for H2S and 0.1 μM for H2O2, with high sensitivities of 61.93 μA cm-2 mM-1 for H2S, and 75.96 μA cm-2 mM-1 for H2O2. The integration of this biocompatible microelectrode into a custom wireless microfluidic chip enables the construction of a miniature intelligent system for in situ monitoring of H2S and H2O2 released from different living cells to differentiate between cancerous and normal cells. When applied for real-time tracking of H2S and H2O2 secreted by colorectal cancer tissues, it allows the evaluation of their chemotherapeutic efficacy. These findings hold paramount implications for disease diagnosis and therapy.
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Affiliation(s)
- Yun Xu
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science & Technology, Wuhan, 430074, China; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, 637457, Singapore
| | - Wei Huang
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science & Technology, Wuhan, 430074, China
| | - Hongwei Duan
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, 637457, Singapore.
| | - Fei Xiao
- Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science & Technology, Wuhan, 430074, China.
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2
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Xu Z, Li W, Dong X, Chen Y, Zhang D, Wang J, Zhou L, He G. Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence. Clin Chim Acta 2024; 559:119686. [PMID: 38663471 DOI: 10.1016/j.cca.2024.119686] [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: 11/27/2023] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 05/03/2024]
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.
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Affiliation(s)
- Zishan Xu
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Wei Li
- School of Forensic Medicine, Xinxiang Medical University, Xinxiang 453000, China
| | - Xiangyang Dong
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Yingying Chen
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang 453000, China
| | - Dan Zhang
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China
| | - Jingnan Wang
- Xinxiang Medical University SanQuan Medical College, Xinxiang 453003, China
| | - Lin Zhou
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Guoyang He
- Department of Pathology, Xinxiang Medical University, Xinxiang 453000, China.
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Xu H, Usuyama N, Bagga J, Zhang S, Rao R, Naumann T, Wong C, Gero Z, González J, Gu Y, Xu Y, Wei M, Wang W, Ma S, Wei F, Yang J, Li C, Gao J, Rosemon J, Bower T, Lee S, Weerasinghe R, Wright BJ, Robicsek A, Piening B, Bifulco C, Wang S, Poon H. A whole-slide foundation model for digital pathology from real-world data. Nature 2024; 630:181-188. [PMID: 38778098 PMCID: PMC11153137 DOI: 10.1038/s41586-024-07441-w] [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: 11/30/2023] [Accepted: 04/19/2024] [Indexed: 05/25/2024]
Abstract
Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.
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Affiliation(s)
- Hanwen Xu
- Microsoft Research, Redmond, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | | | | | | | | | | | | | | | | | - Yu Gu
- Microsoft Research, Redmond, WA, USA
| | - Yanbo Xu
- Microsoft Research, Redmond, WA, USA
| | - Mu Wei
- Microsoft Research, Redmond, WA, USA
| | | | | | - Furu Wei
- Microsoft Research, Redmond, WA, USA
| | | | | | | | | | | | - Soohee Lee
- Providence Research Network, Renton, WA, USA
| | | | | | | | - Brian Piening
- Providence Genomics, Portland, OR, USA
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Carlo Bifulco
- Providence Genomics, Portland, OR, USA.
- Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA.
| | - Sheng Wang
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Department of Surgery, University of Washington, Seattle, WA, USA.
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4
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Shao W, Shi H, Liu J, Zuo Y, Sun L, Xia T, Chen W, Wan P, Sheng J, Zhu Q, Zhang D. Multi-Instance Multi-Task Learning for Joint Clinical Outcome and Genomic Profile Predictions From the Histopathological Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2266-2278. [PMID: 38319755 DOI: 10.1109/tmi.2024.3362852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients' survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.
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Uddin AH, Chen YL, Akter MR, Ku CS, Yang J, Por LY. Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures. Heliyon 2024; 10:e30625. [PMID: 38742084 PMCID: PMC11089372 DOI: 10.1016/j.heliyon.2024.e30625] [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: 02/03/2024] [Revised: 04/02/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024] Open
Abstract
Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and CSI of 0.7556, 0.8839, 0.8796, and 0.7140) on the IQ-OTHNCCD dataset, exceeding recent benchmarks and aligning with other reported results. Efficiency evaluations were conducted in various scenarios. For instance, training on only 10 % of LC25000 resulted in high accuracy rates of 99.19 % (J, MCC, Kp, and CSI of 0.9840, 0.9898, 0.9898, and 0.9837) (D1) and 99.30 % (J, MCC, Kp, and CSI of 0.9863, 0.9913, 0.9913, and 0.9861) (D2). In NCT-CRC-HE-100K, D2 achieved an impressive 99.53 % accuracy (J, MCC, Kp, and CSI of 0.9906, 0.9946, 0.9946, and 0.9906) with training on only 30 % of the dataset and testing on the remaining 70 %. When tested on CRC-VAL-HE-7K, D1 and D2 achieved 95 % accuracy (J, MCC, Kp, and CSI of 0.8845, 0.9455, 0.9452, and 0.8745) and 96 % accuracy (J, MCC, Kp, and CSI of 0.8926, 0.9504, 0.9503, and 0.8798), respectively, outperforming previously reported results and aligning closely with others. Lastly, training D2 on just 10 % of NCT-CRC-HE-100K and testing on CRC-VAL-HE-7K resulted in significant outperformance of InceptionV3, Xception, and DenseNet201 benchmarks, achieving an accuracy rate of 82.98 % (J, MCC, Kp, and CSI of 0.7227, 0.8095, 0.8081, and 0.6671). Finally, using explainable AI algorithms such as Grad-CAM, Grad-CAM++, Score-CAM, and Faster Score-CAM, along with their emphasized versions, we visualized the features from the last layer of DenseNet201 for histopathological as well as CT-scan image samples. The proposed dense models, with their multi-modality, robustness, and efficiency in cancer image classification, hold the promise of significant advancements in medical diagnostics. They have the potential to revolutionize early cancer detection and improve healthcare accessibility worldwide.
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Affiliation(s)
- A. Hasib Uddin
- Department of Computer Science and Engineering, Khwaja Yunus Ali University, Enayetpur, Chouhali, Sirajganj, 6751, Bangladesh
| | - Yen-Lin Chen
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 106344, Taiwan
| | - Miss Rokeya Akter
- Department of Computer Science and Engineering, Khwaja Yunus Ali University, Enayetpur, Chouhali, Sirajganj, 6751, Bangladesh
| | - Chin Soon Ku
- Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar, 31900, Malaysia
| | - Jing Yang
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lip Yee Por
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
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6
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Xu J, Huang D, Zhang X. scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307835. [PMID: 38483032 PMCID: PMC11109621 DOI: 10.1002/advs.202307835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/24/2024] [Indexed: 05/23/2024]
Abstract
Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- University of Chinese Academy of SciencesBeijing100049China
| | - De‐Shuang Huang
- Eastern Institute for Advanced StudyEastern Institute of TechnologyNingbo315200China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- Center of Economic BotanyCore Botanical GardensChinese Academy of SciencesWuhan430074China
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Zhou H, Watson M, Bernadt CT, Lin SS, Lin CY, Ritter JH, Wein A, Mahler S, Rawal S, Govindan R, Yang C, Cote RJ. AI-guided histopathology predicts brain metastasis in lung cancer patients. J Pathol 2024; 263:89-98. [PMID: 38433721 DOI: 10.1002/path.6263] [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: 07/12/2023] [Revised: 11/30/2023] [Accepted: 01/16/2024] [Indexed: 03/05/2024]
Abstract
Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 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)
- Haowen Zhou
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Mark Watson
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Cory T Bernadt
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Steven Siyu Lin
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Chieh-Yu Lin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jon H Ritter
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Alexander Wein
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Simon Mahler
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Sid Rawal
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Ramaswamy Govindan
- Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA
| | - Changhuei Yang
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Richard J Cote
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
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Mu Y, Tizhoosh HR, Dehkharghanian T, Alfasly S, Campbell CJV. Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:721-734. [PMID: 38320631 DOI: 10.1016/j.ajpath.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/29/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024]
Abstract
Histopathology is the reference standard for pathology diagnosis, and has evolved with the digitization of glass slides [ie, whole slide images (WSIs)]. While trained histopathologists are able to diagnose diseases by examining WSIs visually, this process is time consuming and prone to variability. To address these issues, artificial intelligence models are being developed to generate slide-level representations of WSIs, summarizing the entire slide as a single vector. This enables various computational pathology applications, including interslide search, multimodal training, and slide-level classification. Achieving expressive and robust slide-level representations hinges on patch feature extraction and aggregation steps. This study proposed an additional binary patch grouping (BPG) step, a plugin that can be integrated into various slide-level representation pipelines, to enhance the quality of slide-level representation in bone marrow histopathology. BPG excludes patches with less clinical relevance through minimal interaction with the pathologist; a one-time human intervention for the entire process. This study further investigated domain-general versus domain-specific feature extraction models based on convolution and attention and examined two different feature aggregation methods, with and without BPG, showing BPG's generalizability. The results showed that using BPG boosts the performance of WSI retrieval (mean average precision at 10) by 4% and improves WSI classification (weighted-F1) by 5% compared to not using BPG. Additionally, domain-general large models and parameterized pooling produced the best-quality slide-level representations.
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Affiliation(s)
- Youqing Mu
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Hamid R Tizhoosh
- Rhazes Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Taher Dehkharghanian
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Nephrology, University Health Network, Toronto, Ontario, Canada
| | - Saghir Alfasly
- Rhazes Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Clinton J V Campbell
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.
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10
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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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11
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Kiran N, Yashaswini C, Maheshwari R, Bhattacharya S, Prajapati BG. Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies. ACS Pharmacol Transl Sci 2024; 7:967-990. [PMID: 38633600 PMCID: PMC11019743 DOI: 10.1021/acsptsci.4c00008] [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: 01/10/2024] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 04/19/2024]
Abstract
Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.
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Affiliation(s)
- Neelakanta
Sarvashiva Kiran
- Department
of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, Karnataka 560064, India
| | - Chandrashekar Yashaswini
- Department
of Biotechnology, School of Applied Sciences, REVA University, Bengaluru, Karnataka 560064, India
| | - Rahul Maheshwari
- School
of Pharmacy and Technology Management, SVKM’s
Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-University, Green Industrial Park, TSIIC,, Jadcherla, Hyderabad 509301, India
| | - Sankha Bhattacharya
- School
of Pharmacy and Technology Management, SVKM’S
NMIMS Deemed-to-be University, Shirpur, Maharashtra 425405, India
| | - Bhupendra G. Prajapati
- Shree.
S. K. Patel College of Pharmaceutical Education and Research, Ganpat University, Kherva, Gujarat 384012, India
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12
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Wu X, Li W, Tu H. Big data and artificial intelligence in cancer research. Trends Cancer 2024; 10:147-160. [PMID: 37977902 DOI: 10.1016/j.trecan.2023.10.006] [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: 08/31/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/19/2023]
Abstract
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.
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Affiliation(s)
- Xifeng Wu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wenyuan Li
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Huakang Tu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
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13
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Azadi Moghadam P, Bashashati A, Goldenberg SL. Artificial Intelligence and Pathomics: Prostate Cancer. Urol Clin North Am 2024; 51:15-26. [PMID: 37945099 DOI: 10.1016/j.ucl.2023.06.001] [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: 11/12/2023]
Abstract
Artificial intelligence (AI) has the potential to transform pathologic diagnosis and cancer patient management as a predictive and prognostic biomarker. AI-based systems can be used to examine digitally scanned histopathology slides and differentiate benign from malignant cells and low from high grade. Deep learning models can analyze patient data from individual or multimodal combinations and identify patterns to be used to predict the response to different therapeutic options, the risk of recurrence or progression, and the prognosis of the newly diagnosed patient. AI-based models will improve treatment planning for patients with prostate cancer and improve the efficiency and cost-effectiveness of the pathology laboratory.
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Affiliation(s)
- Puria Azadi Moghadam
- Department of Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada
| | - Ali Bashashati
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z7, Canada
| | - S Larry Goldenberg
- Department of Urologic Sciences, University of British Columbia, 2775 Laurel Street, Vancouver British Columbia V5Z 1M9, Canada.
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14
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Chen YY, Fei F, Ding LL, Wen SY, Ren CF, Gong AH. Integrated gut microbiome and metabolome analysis reveals the inhibition effect of Lactobacillus plantarum CBT against colorectal cancer. Food Funct 2024; 15:853-865. [PMID: 38164977 DOI: 10.1039/d3fo04806c] [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: 01/03/2024]
Abstract
The microecological stability of the gut microbiota plays a pivotal role in both preventing and treating colorectal cancer (CRC). This study investigated whether Lactobacillus plantarum CBT (LP-CBT) prevents CRC by inducing alterations in the gut microbiota composition and associated metabolites. The results showed that LP-CBT inhibited colorectal tumorigenesis in azoxymethane/dextran sulfate sodium (AOM/DSS)-treated mice by repairing the intestinal barrier function. Furthermore, LP-CBT decreased pro-inflammatory cytokines and anti-inflammatory cytokines. Importantly, LP-CBT remodeled intestinal homeostasis by increasing probiotics (Coprococcus, Mucispirillum, and Lactobacillus) and reducing harmful bacteria (Dorea, Shigella, Alistipes, Paraprevotella, Bacteroides, Sutterella, Turicibacter, Bifidobacterium, Clostridium, Allobaculum), significantly influencing arginine biosynthesis. Therefore, LP-CBT treatment regulated invertases and metabolites associated with the arginine pathway (carbamoyl phosphate, carboxymethyl proline, L-lysine, 10,11-epoxy-3-geranylgeranylindole, n-(6)-[(indol-3-yl)acetyl]-L-lysine, citrulline, N2-succinyl-L-ornithine, and (5-L-glutamyl)-L-glutamate). Furthermore, the inhibitory effect of LP-CBT on colorectal cancer was further confirmed using the MC38 subcutaneous tumor model. Collectively, these findings offer compelling evidence supporting the potential of LP-CBT as a viable preventive strategy against CRC.
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Affiliation(s)
- Yan-Yan Chen
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212003, China.
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao, SAR 999078, China
- Hematological Disease Institute of Jiangsu University, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, 212003, China
| | - Fei Fei
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212003, China.
| | - Ling-Ling Ding
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212003, China.
| | - Shi-Yuan Wen
- College of Basic Medical Sciences, Shanxi Medical University, Taiyuan 030000, China.
| | - Cai-Fang Ren
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212003, China.
| | - Ai-Hua Gong
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212003, China.
- Hematological Disease Institute of Jiangsu University, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, 212003, China
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15
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Hang D, Knudsen MD, Song M. Moving Toward Personalized Colorectal Cancer Follow-Up Care. JAMA Oncol 2024; 10:29-31. [PMID: 37971198 DOI: 10.1001/jamaoncol.2023.5072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Affiliation(s)
- Dong Hang
- Department of Epidemiology, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Markus Dines Knudsen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Mingyang Song
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston
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16
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Jia Y, Liu J, Chen L, Zhao T, Wang Y. THItoGene: a deep learning method for predicting spatial transcriptomics from histological images. Brief Bioinform 2023; 25:bbad464. [PMID: 38145948 PMCID: PMC10749789 DOI: 10.1093/bib/bbad464] [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: 10/15/2023] [Revised: 11/10/2023] [Accepted: 11/18/2023] [Indexed: 12/27/2023] Open
Abstract
Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.
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Affiliation(s)
- Yuran Jia
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150040, China
| | - Junliang Liu
- Institute for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150040, China
| | - Li Chen
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Tianyi Zhao
- School of Medicine and Health, Harbin Institute of Technology, Harbin, 150040, China
| | - Yadong Wang
- School of Medicine and Health, Harbin Institute of Technology, Harbin, 150040, China
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17
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Gao X, Yang H, Chu Y, Zhang W, Wang Z, Ji L. The specific viral composition in triple-negative breast cancer tissue shapes the specific tumor microenvironment characterized on pathological images. Microb Pathog 2023; 184:106385. [PMID: 37813319 DOI: 10.1016/j.micpath.2023.106385] [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: 08/13/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 10/11/2023]
Abstract
Numerous studies have shown that different subtypes of breast cancer (BC) have significant differences in terms of the tumor microbiome, host gene expression, and histopathological image, whereas the biological links between these cancer-associated indicators are still unknown. Here, we performed a comprehensive analysis with 610 patients of the four subtypes of BC with matched tissue microbiota, host transcriptome, and histopathological image samples. Correlation analysis showed that the composition of intratumoral viruses shaped the tumor microenvironment (TME) of patients with BC, and the TME was further reflected in the histopathological images. Of the four subtypes, patients with triple-negative breast cancer (TNBC) had unique intratumoral viral community composition, non-cancer cell infiltration in the TME, and histopathological image characteristics. Furthermore, we detected multiple virus-cell-image association axes in TNBC, in which tumor-associated macrophages (TAMs) have clinical prognostic implication. This study provides a comprehensive map of the associations between the intratumoral virome, TME, and histopathological image of TNBC, as well as insights into disease prognosis that can be crucial for precise therapeutic intervention strategies.
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Affiliation(s)
- Xuzhu Gao
- Institute of Clinical Oncology, The Second People's Hospital of Lianyungang City (Cancer Hospital of Lianyungang), Lianyungang, China; Department of Central Laboratory, Lianyungang Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China
| | - Hailong Yang
- Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China; School of Electrical & Information Engineering, Anhui University of Technology, Anhui, 243002, China
| | - Yuwen Chu
- Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China; School of Electrical & Information Engineering, Anhui University of Technology, Anhui, 243002, China
| | - Wenjing Zhang
- Tandon School of Engineering, New York University, New York, NY, 11201, USA
| | - Zhongchen Wang
- Department of General Surgery, Daqing Longnan Hospital, The Fifth Affiliated Hospital of Qiqihar Medical College, Daqing, 163453, China
| | - Lei Ji
- Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China.
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18
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Nasrallah MP, Zhao J, Tsai CC, Meredith D, Marostica E, Ligon KL, Golden JA, Yu KH. Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma. MED 2023; 4:526-540.e4. [PMID: 37421953 PMCID: PMC10527821 DOI: 10.1016/j.medj.2023.06.002] [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: 11/27/2022] [Revised: 04/17/2023] [Accepted: 06/06/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND Timely and accurate intraoperative cryosection evaluations remain the gold standard for guiding surgical treatments for gliomas. However, the tissue-freezing process often generates artifacts that make histologic interpretation difficult. In addition, the 2021 WHO Classification of Tumors of the Central Nervous System incorporates molecular profiles in the diagnostic categories, so standard visual evaluation of cryosections alone cannot completely inform diagnoses based on the new classification system. METHODS To address these challenges, we develop the context-aware Cryosection Histopathology Assessment and Review Machine (CHARM) using samples from 1,524 glioma patients from three different patient populations to systematically analyze cryosection slides. FINDINGS Our CHARM models successfully identified malignant cells (AUROC = 0.98 ± 0.01 in the independent validation cohort), distinguished isocitrate dehydrogenase (IDH)-mutant tumors from wild type (AUROC = 0.79-0.82), classified three major types of molecularly defined gliomas (AUROC = 0.88-0.93), and identified the most prevalent subtypes of IDH-mutant tumors (AUROC = 0.89-0.97). CHARM further predicts clinically important genetic alterations in low-grade glioma, including ATRX, TP53, and CIC mutations, CDKN2A/B homozygous deletion, and 1p/19q codeletion via cryosection images. CONCLUSIONS Our approaches accommodate the evolving diagnostic criteria informed by molecular studies, provide real-time clinical decision support, and will democratize accurate cryosection diagnoses. FUNDING Supported in part by the National Institute of General Medical Sciences grant R35GM142879, the Google Research Scholar Award, the Blavatnik Center for Computational Biomedicine Award, the Partners' Innovation Discovery Grant, and the Schlager Family Award for Early Stage Digital Health Innovations.
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Affiliation(s)
- MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Junhan Zhao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Cheng Che Tsai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - David Meredith
- Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Eliana Marostica
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology, Boston, MA 02139, USA
| | - Keith L Ligon
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jeffrey A Golden
- Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
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19
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Zang L, Liang W, Ke H, Chen F, Shen C. Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet. Sci Rep 2023; 13:12779. [PMID: 37550341 PMCID: PMC10406939 DOI: 10.1038/s41598-023-39240-0] [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: 03/10/2023] [Accepted: 07/21/2023] [Indexed: 08/09/2023] Open
Abstract
As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network (PCNN) algorithm is used to filter the image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The dataset is a combination of Hainan Provincial People's Hospital and some public datasets Lits. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.
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Affiliation(s)
- Lan Zang
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China
- School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
| | - Wei Liang
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China
- School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
| | - Hanchu Ke
- School of Electronic Science and Technology, Hainan University, Haikou, 570288, China.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570216, China.
| | - Chong Shen
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China.
- School of Electronic Science and Technology, Hainan University, Haikou, 570288, China.
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20
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Choi S, Kim S. Artificial Intelligence in the Pathology of Gastric Cancer. J Gastric Cancer 2023; 23:410-427. [PMID: 37553129 PMCID: PMC10412971 DOI: 10.5230/jgc.2023.23.e25] [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: 05/29/2023] [Revised: 07/09/2023] [Accepted: 07/14/2023] [Indexed: 08/10/2023] Open
Abstract
Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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21
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Pierre K, Gupta M, Raviprasad A, Sadat Razavi SM, Patel A, Peters K, Hochhegger B, Mancuso A, Forghani R. Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges. Expert Rev Anticancer Ther 2023; 23:1265-1279. [PMID: 38032181 DOI: 10.1080/14737140.2023.2286001] [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: 09/01/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential. AREAS COVERED The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential. EXPERT OPINION There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Manas Gupta
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
| | - Abheek Raviprasad
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Seyedeh Mehrsa Sadat Razavi
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Anjali Patel
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Keith Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
- Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL, USA
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22
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Fettucciari K, Fruganti A, Stracci F, Spaterna A, Marconi P, Bassotti G. Clostridioides difficile Toxin B Induced Senescence: A New Pathologic Player for Colorectal Cancer? Int J Mol Sci 2023; 24:ijms24098155. [PMID: 37175861 PMCID: PMC10179142 DOI: 10.3390/ijms24098155] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/26/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Clostridioides difficile (C. difficile) is responsible for a high percentage of gastrointestinal infections and its pathological activity is due to toxins A and B. C. difficile infection (CDI) is increasing worldwide due to the unstoppable spread of C. difficile in the anthropized environment and the progressive human colonization. The ability of C. difficile toxin B to induce senescent cells and the direct correlation between CDI, irritable bowel syndrome (IBS), and inflammatory bowel diseases (IBD) could cause an accumulation of senescent cells with important functional consequences. Furthermore, these senescent cells characterized by long survival could push pre-neoplastic cells originating in the colon towards the complete neoplastic transformation in colorectal cancer (CRC) by the senescence-associated secretory phenotype (SASP). Pre-neoplastic cells could appear as a result of various pro-carcinogenic events, among which, are infections with bacteria that produce genotoxins that generate cells with high genetic instability. Therefore, subjects who develop IBS and/or IBD after CDI should be monitored, especially if they then have further CDI relapses, waiting for the availability of senolytic and anti-SASP therapies to resolve the pro-carcinogenic risk due to accumulation of senescent cells after CDI followed by IBS and/or IBD.
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Affiliation(s)
- Katia Fettucciari
- Biosciences & Medical Embryology Section, Department of Medicine and Surgery, University of Perugia, 06129 Perugia, Italy
| | - Alessandro Fruganti
- School of Biosciences and Veterinary Medicine, University of Camerino, 62024 Matelica, Italy
| | - Fabrizio Stracci
- Public Health Section, Department of Medicine and Surgery, University of Perugia, 06129 Perugia, Italy
| | - Andrea Spaterna
- School of Biosciences and Veterinary Medicine, University of Camerino, 62024 Matelica, Italy
| | - Pierfrancesco Marconi
- Biosciences & Medical Embryology Section, Department of Medicine and Surgery, University of Perugia, 06129 Perugia, Italy
| | - Gabrio Bassotti
- Gastroenterology, Hepatology & Digestive Endoscopy Section, Department of Medicine and Surgery, University of Perugia, 06129 Perugia, Italy
- Gastroenterology & Hepatology Unit, Santa Maria Della Misericordia Hospital, 06129 Perugia, Italy
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