1
|
Volinsky-Fremond S, Horeweg N, Andani S, Barkey Wolf J, Lafarge MW, de Kroon CD, Ørtoft G, Høgdall E, Dijkstra J, Jobsen JJ, Lutgens LCHW, Powell ME, Mileshkin LR, Mackay H, Leary A, Katsaros D, Nijman HW, de Boer SM, Nout RA, de Bruyn M, Church D, Smit VTHBM, Creutzberg CL, Koelzer VH, Bosse T. Prediction of recurrence risk in endometrial cancer with multimodal deep learning. Nat Med 2024:10.1038/s41591-024-02993-w. [PMID: 38789645 DOI: 10.1038/s41591-024-02993-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 04/11/2024] [Indexed: 05/26/2024]
Abstract
Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan-Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.
Collapse
Affiliation(s)
| | - Nanda Horeweg
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Sonali Andani
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University Hospital, University of Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jurriaan Barkey Wolf
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Maxime W Lafarge
- Department of Pathology and Molecular Pathology, University Hospital, University of Zurich, Zurich, Switzerland
| | - Cor D de Kroon
- Department of Gynecology and Obstetrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Gitte Ørtoft
- Department of Gynecology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Estrid Høgdall
- Department of Pathology, Herlev University Hospital, Herlev, Denmark
| | - Jouke Dijkstra
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan J Jobsen
- Department of Radiation Oncology, Medisch Spectrum Twente, Enschede, The Netherlands
| | | | - Melanie E Powell
- Department of Clinical Oncology, Barts Health NHS Trust, London, UK
| | - Linda R Mileshkin
- Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
| | - Helen Mackay
- Department of Medical Oncology and Hematology, Odette Cancer Center Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Alexandra Leary
- Department Medical Oncology, Gustave Roussy Institute, Villejuif, France
| | - Dionyssios Katsaros
- Department of Surgical Sciences, Gynecologic Oncology, Città della Salute and S Anna Hospital, University of Turin, Turin, Italy
| | - Hans W Nijman
- Department of Obstetrics and Gynecology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stephanie M de Boer
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Remi A Nout
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marco de Bruyn
- Department of Obstetrics and Gynecology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - David Church
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Comprehensive Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Vincent T H B M Smit
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Carien L Creutzberg
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Viktor H Koelzer
- Department of Pathology and Molecular Pathology, University Hospital, University of Zurich, Zurich, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Tjalling Bosse
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
| |
Collapse
|
2
|
Umemoto M, Mariya T, Nambu Y, Nagata M, Horimai T, Sugita S, Kanaseki T, Takenaka Y, Shinkai S, Matsuura M, Iwasaki M, Hirohashi Y, Hasegawa T, Torigoe T, Fujino Y, Saito T. Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms. Cancers (Basel) 2024; 16:1810. [PMID: 38791889 PMCID: PMC11119770 DOI: 10.3390/cancers16101810] [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: 03/07/2024] [Revised: 04/22/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.
Collapse
Affiliation(s)
- Mina Umemoto
- Department of Obstetrics and Gynecology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (M.U.); (Y.T.); (S.S.); (M.M.); (M.I.); (T.S.)
| | - Tasuku Mariya
- Department of Obstetrics and Gynecology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (M.U.); (Y.T.); (S.S.); (M.M.); (M.I.); (T.S.)
| | - Yuta Nambu
- Department of Media Architecture, Future University Hakodate, Hakodate 041-8655, Japan; (Y.N.); (M.N.); (Y.F.)
| | - Mai Nagata
- Department of Media Architecture, Future University Hakodate, Hakodate 041-8655, Japan; (Y.N.); (M.N.); (Y.F.)
| | | | - Shintaro Sugita
- Department of Surgical Pathology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (S.S.); (T.H.)
| | - Takayuki Kanaseki
- Department of Pathology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (T.K.); (Y.H.); (T.T.)
| | - Yuka Takenaka
- Department of Obstetrics and Gynecology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (M.U.); (Y.T.); (S.S.); (M.M.); (M.I.); (T.S.)
| | - Shota Shinkai
- Department of Obstetrics and Gynecology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (M.U.); (Y.T.); (S.S.); (M.M.); (M.I.); (T.S.)
| | - Motoki Matsuura
- Department of Obstetrics and Gynecology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (M.U.); (Y.T.); (S.S.); (M.M.); (M.I.); (T.S.)
| | - Masahiro Iwasaki
- Department of Obstetrics and Gynecology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (M.U.); (Y.T.); (S.S.); (M.M.); (M.I.); (T.S.)
| | - Yoshihiko Hirohashi
- Department of Pathology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (T.K.); (Y.H.); (T.T.)
| | - Tadashi Hasegawa
- Department of Surgical Pathology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (S.S.); (T.H.)
| | - Toshihiko Torigoe
- Department of Pathology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (T.K.); (Y.H.); (T.T.)
| | - Yuichi Fujino
- Department of Media Architecture, Future University Hakodate, Hakodate 041-8655, Japan; (Y.N.); (M.N.); (Y.F.)
| | - Tsuyoshi Saito
- Department of Obstetrics and Gynecology, Sapporo Medical University of Medicine, Sapporo 060-8556, Japan; (M.U.); (Y.T.); (S.S.); (M.M.); (M.I.); (T.S.)
| |
Collapse
|
3
|
Syed RU, Afsar S, Aboshouk NAM, Salem Alanzi S, Abdalla RAH, Khalifa AAS, Enrera JA, Elafandy NM, Abdalla RAH, Ali OHH, Satheesh Kumar G, Alshammari MD. LncRNAs in necroptosis: Deciphering their role in cancer pathogenesis and therapy. Pathol Res Pract 2024; 256:155252. [PMID: 38479121 DOI: 10.1016/j.prp.2024.155252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 04/14/2024]
Abstract
Necroptosis, a controlled type of cell death that is different from apoptosis, has become a key figure in the aetiology of cancer and offers a possible target for treatment. A growing number of biological activities, including necroptosis, have been linked to long noncoding RNAs (lncRNAs), a varied family of RNA molecules with limited capacity to code for proteins. The complex interactions between LncRNAs and important molecular effectors of necroptosis, including mixed lineage kinase domain-like pseudokinase (MLKL) and receptor-interacting protein kinase 3 (RIPK3), will be investigated. We will explore the many methods that LncRNAs use to affect necroptosis, including protein-protein interactions, transcriptional control, and post-transcriptional modification. Additionally, the deregulation of certain LncRNAs in different forms of cancer will be discussed, highlighting their dual function in influencing necroptotic processes as tumour suppressors and oncogenes. The goal of this study is to thoroughly examine the complex role that LncRNAs play in controlling necroptotic pathways and how that regulation affects the onset and spread of cancer. In the necroptosis for cancer treatment, this review will also provide insight into the possible therapeutic uses of targeting LncRNAs. Techniques utilising LncRNA-based medicines show promise in controlling necroptotic pathways to prevent cancer from spreading and improve the effectiveness of treatment.
Collapse
Affiliation(s)
- Rahamat Unissa Syed
- Department of Pharmaceutics, College of Pharmacy, University of Ha'il, Hail 81442, Saudi Arabia.
| | - S Afsar
- Department of Virology, Sri Venkateswara University, Tirupathi, Andhra Pradesh 517502, India.
| | - Nayla Ahmed Mohammed Aboshouk
- Department of Clinical laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 81442, Saudi Arabia
| | | | | | - Amna Abakar Suleiman Khalifa
- Department of Clinical laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 81442, Saudi Arabia
| | - Jerlyn Apatan Enrera
- Department of Clinical laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 81442, Saudi Arabia
| | - Nancy Mohammad Elafandy
- Department of Clinical laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 81442, Saudi Arabia
| | - Randa Abdeen Husien Abdalla
- Department of Clinical laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 81442, Saudi Arabia
| | - Omar Hafiz Haj Ali
- Department of Clinical laboratory Sciences, College of Applied Medical Sciences, University of Hail, Hail 81442, Saudi Arabia
| | - G Satheesh Kumar
- Department of Pharmaceutical Chemistry, College of Pharmacy, Seven Hills College of Pharmacy, Venkataramapuram, Tirupati, India
| | - Maali D Alshammari
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Hail, Hail 81442, Saudi Arabia
| |
Collapse
|
4
|
Whangbo J, Lee YS, Kim YJ, Kim J, Kim KG. Predicting Mismatch Repair Deficiency Status in Endometrial Cancer through Multi-Resolution Ensemble Learning in Digital Pathology. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-00997-z. [PMID: 38378964 DOI: 10.1007/s10278-024-00997-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 02/22/2024]
Abstract
For molecular classification of endometrial carcinoma, testing for mismatch repair (MMR) status is becoming a routine process. Mismatch repair deficiency (MMR-D) is caused by loss of expression in one or more of the 4 major MMR proteins: MLH1, MSH2, MSH6, PHS2. Over 30% of patients with endometrial cancer have MMR-D. Determining the MMR status holds significance as individuals with MMR-D are potential candidates for immunotherapy. Pathological whole slide image (WSI) of endometrial cancer with immunohistochemistry results of MMR proteins were gathered. Color normalization was applied to the tiles using a CycleGAN-based network. The WSI was divided into tiles at three different magnifications (2.5 × , 5 × , and 10 ×). Three distinct networks of the same architecture were employed to include features from all three magnification levels and were stacked for ensemble learning. Three architectures, InceptionResNetV2, EfficientNetB2, and EfficientNetB3 were employed and subjected to comparison. The per-tile results were gathered to classify MMR status in the WSI, and prediction accuracy was evaluated using the following performance metrics: AUC, accuracy, sensitivity, and specificity. The EfficientNetB2 was able to make predictions with an AUC of 0.821, highest among the three architectures, and an overall AUC range of 0.767 - 0.821 was reported across the three architectures. In summary, our study successfully predicted MMR classification from pathological WSIs in endometrial cancer through a multi-resolution ensemble learning approach, which holds the potential to facilitate swift decisions on tailored treatment, such as immunotherapy, in clinical settings.
Collapse
Affiliation(s)
- Jongwook Whangbo
- Department of Computer Science, Wesleyan University, Middletown, Connecticut, USA
- Medical Devices R&D Center, Gachon University Gil Hospital, Incheon, Republic of Korea
| | - Young Seop Lee
- Medical Devices R&D Center, Gachon University Gil Hospital, Incheon, Republic of Korea
| | - Young Jae Kim
- Medical Devices R&D Center, Gachon University Gil Hospital, Incheon, Republic of Korea
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health & Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
| | - Jisup Kim
- Department of Pathology, Gil Medical Center, Gachon University College of Medicine, 38-13, Dokjeom-Ro 3Beon-Gil, Namdong-Gu, Incheon, Republic of Korea.
| | - Kwang Gi Kim
- Medical Devices R&D Center, Gachon University Gil Hospital, Incheon, Republic of Korea.
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health & Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea.
- Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon, Republic of Korea.
| |
Collapse
|
5
|
Yang X, Yang C, Zhang S, Geng H, Zhu AX, Bernards R, Qin W, Fan J, Wang C, Gao Q. Precision treatment in advanced hepatocellular carcinoma. Cancer Cell 2024; 42:180-197. [PMID: 38350421 DOI: 10.1016/j.ccell.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/01/2023] [Accepted: 01/17/2024] [Indexed: 02/15/2024]
Abstract
The past decade has witnessed significant advances in the systemic treatment of advanced hepatocellular carcinoma (HCC). Nevertheless, the newly developed treatment strategies have not achieved universal success and HCC patients frequently exhibit therapeutic resistance to these therapies. Precision treatment represents a paradigm shift in cancer treatment in recent years. This approach utilizes the unique molecular characteristics of individual patient to personalize treatment modalities, aiming to maximize therapeutic efficacy while minimizing side effects. Although precision treatment has shown significant success in multiple cancer types, its application in HCC remains in its infancy. In this review, we discuss key aspects of precision treatment in HCC, including therapeutic biomarkers, molecular classifications, and the heterogeneity of the tumor microenvironment. We also propose future directions, ranging from revolutionizing current treatment methodologies to personalizing therapy through functional assays, which will accelerate the next phase of advancements in this area.
Collapse
Affiliation(s)
- Xupeng Yang
- Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Chen Yang
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Immune Regulation in Cancer Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Shu Zhang
- Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Haigang Geng
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Andrew X Zhu
- I-Mab Biopharma, Shanghai, China; Jiahui International Cancer Center, Jiahui Health, Shanghai, China
| | - René Bernards
- Division of Molecular Carcinogenesis, Oncode Institute, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Wenxin Qin
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Cun Wang
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
| |
Collapse
|
6
|
Schumann Y, Dottermusch M, Schweizer L, Krech M, Lempertz T, Schüller U, Neumann P, Neumann JE. Morphology-based molecular classification of spinal cord ependymomas using deep neural networks. Brain Pathol 2024:e13239. [PMID: 38205683 DOI: 10.1111/bpa.13239] [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: 09/07/2023] [Accepted: 12/31/2023] [Indexed: 01/12/2024] Open
Abstract
Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (nMPE = 84, nSP-EPN = 55). Self-supervised and weakly-supervised neural networks were used for classification. We employed attention analysis and supervised machine-learning methods for the discovery and quantification of morphological features and their correlation to the diagnoses of experienced neuropathologists. Our best performing model predicted the DNA methylation class with 98% test accuracy and used self-supervised learning to outperform pretrained encoder-networks (86% test accuracy). In contrast, the diagnoses of neuropathologists matched the DNA methylation class in only 83% of cases. Domain-adaptation techniques improved model generalization to an external validation cohort by up to 22%. Statistically significant morphological features were identified per molecular type and quantitatively correlated to human diagnoses. The approach was extended to recently defined subtypes of myxopapillary ependymomas (MPE-(A/B), 80% test accuracy). In summary, we demonstrated the accurate prediction of the DNA methylation class of spinal cord ependymomas (SP-EPN, MPE(-A/B)) using hematoxylin and eosin stained whole-slide images. Our approach may prospectively serve as a supplementary resource for integrated diagnostics and may even help to establish a standardized, high-quality level of histology-based diagnostics across institutions-in particular in low-income countries, where expensive DNA-methylation analyses may not be readily available.
Collapse
Affiliation(s)
- Yannis Schumann
- Chair for High Performance Computing, Helmut-Schmidt-University Hamburg, Hamburg, Germany
| | - Matthias Dottermusch
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Institute of Neuropathology, UKE, Hamburg, Germany
| | - Leonille Schweizer
- Institute of Neurology (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Maja Krech
- Institute for Neuropathology, Charité Berlin, Berlin, Germany
| | - Tasja Lempertz
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Ulrich Schüller
- Institute of Neuropathology, UKE, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, UKE, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, UKE, Hamburg, Germany
| | - Philipp Neumann
- Chair for High Performance Computing, Helmut-Schmidt-University Hamburg, Hamburg, Germany
| | - Julia E Neumann
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Institute of Neuropathology, UKE, Hamburg, Germany
| |
Collapse
|
7
|
Tak S, Han G, Leem SH, Lee SY, Paek K, Kim JA. Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis. Front Bioeng Biotechnol 2024; 11:1302983. [PMID: 38268938 PMCID: PMC10806080 DOI: 10.3389/fbioe.2023.1302983] [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: 09/27/2023] [Accepted: 12/28/2023] [Indexed: 01/26/2024] Open
Abstract
Bladder cancer is the most common urological malignancy worldwide, and its high recurrence rate leads to poor survival outcomes. The effect of anticancer drug treatment varies significantly depending on individual patients and the extent of drug resistance. In this study, we developed a validation system based on an organ-on-a-chip integrated with artificial intelligence technologies to predict resistance to anticancer drugs in bladder cancer. As a proof-of-concept, we utilized the gemcitabine-resistant bladder cancer cell line T24 with four distinct levels of drug resistance (parental, early, intermediate, and late). These cells were co-cultured with endothelial cells in a 3D microfluidic chip. A dataset comprising 2,674 cell images from the chips was analyzed using a convolutional neural network (CNN) to distinguish the extent of drug resistance among the four cell groups. The CNN achieved 95.2% accuracy upon employing data augmentation and a step decay learning rate with an initial value of 0.001. The average diagnostic sensitivity and specificity were 90.5% and 96.8%, respectively, and all area under the curve (AUC) values were over 0.988. Our proposed method demonstrated excellent performance in accurately identifying the extent of drug resistance, which can assist in the prediction of drug responses and in determining the appropriate treatment for bladder cancer patients.
Collapse
Affiliation(s)
- Sungho Tak
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
| | - Gyeongjin Han
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Republic of Korea
| | - Sun-Hee Leem
- Department of Biomedical Sciences, Dong-A University, Busan, Republic of Korea
- Department of Health Sciences, The Graduate School of Dong-A University, Busan, Republic of Korea
| | - Sang-Yeop Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Republic of Korea
| | - Kyurim Paek
- Center for Scientific Instrumentation, Korea Basic Science Institute, Daejeon, Republic of Korea
| | - Jeong Ah Kim
- Center for Scientific Instrumentation, Korea Basic Science Institute, Daejeon, Republic of Korea
- Department of Bio-Analytical Science, University of Science and Technology, Daejeon, Republic of Korea
- Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
8
|
Bai L, Wu Y, Li G, Zhang W, Zhang H, Su J. AI-enabled organoids: Construction, analysis, and application. Bioact Mater 2024; 31:525-548. [PMID: 37746662 PMCID: PMC10511344 DOI: 10.1016/j.bioactmat.2023.09.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/09/2023] [Accepted: 09/09/2023] [Indexed: 09/26/2023] Open
Abstract
Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.
Collapse
Affiliation(s)
- Long Bai
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Wenzhou Institute of Shanghai University, Wenzhou, 325000, China
| | - Yan Wu
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Guangfeng Li
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Department of Orthopedics, Shanghai Zhongye Hospital, Shanghai, 201941, China
| | - Wencai Zhang
- Department of Orthopedics, First Affiliated Hospital, Jinan University, Guangzhou, 510632, China
| | - Hao Zhang
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Jiacan Su
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| |
Collapse
|
9
|
Tavolara TE, Su Z, Gurcan MN, Niazi MKK. One label is all you need: Interpretable AI-enhanced histopathology for oncology. Semin Cancer Biol 2023; 97:70-85. [PMID: 37832751 DOI: 10.1016/j.semcancer.2023.09.006] [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: 10/24/2022] [Revised: 09/06/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.
Collapse
Affiliation(s)
- Thomas E Tavolara
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ziyu Su
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - M Khalid Khan Niazi
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| |
Collapse
|
10
|
Geaney A, O'Reilly P, Maxwell P, James JA, McArt D, Salto-Tellez M. Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption. Oncogene 2023; 42:3545-3555. [PMID: 37875656 PMCID: PMC10673711 DOI: 10.1038/s41388-023-02857-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/19/2023] [Accepted: 10/02/2023] [Indexed: 10/26/2023]
Abstract
Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.
Collapse
Affiliation(s)
- Alice Geaney
- Sonraí Analytics, Whitla Medical Building, 97 Lisburn Rd, Belfast, BT9 7BL, UK
| | - Paul O'Reilly
- Sonraí Analytics, Whitla Medical Building, 97 Lisburn Rd, Belfast, BT9 7BL, UK
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Perry Maxwell
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
- Northern Ireland Biobank, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, BT9 7AE, UK
| | - Darragh McArt
- Sonraí Analytics, Whitla Medical Building, 97 Lisburn Rd, Belfast, BT9 7BL, UK
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK.
- Integrated Pathology Unit, Division of Molecular Pathology, The Institute of Cancer Research London, 15 Cotswold Rd, Sutton, SM2 5NG, UK.
| |
Collapse
|
11
|
Liang H, Wang M, Wen Y, Du F, Jiang L, Geng X, Tang L, Yan H. Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks. Sci Rep 2023; 13:17514. [PMID: 37845380 PMCID: PMC10579320 DOI: 10.1038/s41598-023-44828-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 10/12/2023] [Indexed: 10/18/2023] Open
Abstract
This study aimed to evaluate acute pancreatitis (AP) severity using convolutional neural network (CNN) models with enhanced computed tomography (CT) scans. Three-dimensional DenseNet CNN models were developed and trained using the enhanced CT scans labeled with two severity assessment methods: the computed tomography severity index (CTSI) and Atlanta classification. Each labeling method was used independently for model training and validation. Model performance was evaluated using confusion matrices, areas under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, and respective macro-average metrics. A total of 1,798 enhanced CT scans met the inclusion criteria were included in this study. The dataset was randomly divided into a training dataset (n = 1618) and a test dataset (n = 180) with a ratio of 9:1. The DenseNet model demonstrated promising predictions for both CTSI and Atlanta classification-labeled CT scans, with accuracy greater than 0.7 and AUC-ROC greater than 0.8. Specifically, when trained with CT scans labeled using CTSI, the DenseNet model achieved good performance, with a macro-average F1 score of 0.835 and a macro-average AUC-ROC of 0.980. The findings of this study affirm the feasibility of employing CNN models to predict the severity of AP using enhanced CT scans.
Collapse
Affiliation(s)
- Hongyin Liang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
- Sichuan Provincial Key Laboratory of Pancreatic Injury and Repair, Chengdu, 610083, China
| | - Meng Wang
- Department of Traditional Chinese Medicine, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Yi Wen
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
- Sichuan Provincial Key Laboratory of Pancreatic Injury and Repair, Chengdu, 610083, China
| | - Feizhou Du
- Department of Radiology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Li Jiang
- Department of Cardiac Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Xuelong Geng
- Department of Radiology, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
| | - Lijun Tang
- Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China
- Sichuan Provincial Key Laboratory of Pancreatic Injury and Repair, Chengdu, 610083, China
| | - Hongtao Yan
- Department of Liver Transplantation and Hepato-biliary-pancreatic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610016, China.
| |
Collapse
|
12
|
Costache S, Sajin M, Wedden S, D'Arrigo C. A consolidated working classification of gastric cancer for histopathologists (Review). Biomed Rep 2023; 19:58. [PMID: 37614984 PMCID: PMC10442765 DOI: 10.3892/br.2023.1640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 07/04/2023] [Indexed: 08/25/2023] Open
Abstract
Gastric cancer (GC) remains a disease with poor prognosis despite increasing availability of more effective targeted treatment. This may be in part due to the difficulty in selecting patients for appropriate treatment. Conventional taxonomic classifications of GC are ill-suited to make full use of recent advances in personalised therapy. In the past decade a number of molecular classifications have been proposed to address this; however, to date, there has been little implementation in the diagnostic routine. The lack of harmonisation between these classifications, the complexity and unavailability of some of the tests required plus the demands on time and resources, all contribute to poor uptake in the diagnostic routine. In the present study, these classifications were reviewed and an inclusive working classification that includes their main points, focuses on prognosis and treatment options and can be delivered using four on-slide tests (in situ hybridization for Epstein-Barr encoding region and immunohistochemistry for mismatch repair, E-cadherin and p53) is proposed. These tests can be performed on paraffin-embedded tissue and could be available in the majority of histopathology laboratories. The proposed classification also includes reflex testing for specific biomarkers relevant to treatment selection.
Collapse
Affiliation(s)
- Simona Costache
- University of Medicine and Pharmacy ‘Carol Davila’, 020021 Bucharest, Romania
- Poundbury Cancer Institute, Dorchester DT13BJ, UK
| | - Maria Sajin
- University of Medicine and Pharmacy ‘Carol Davila’, 020021 Bucharest, Romania
- University Emergency Hospital Bucharest, 050098 Bucharest, Romania
| | - Sarah Wedden
- Cancer Diagnostic Quality Assurance Services (CADQAS), Dorchester DT13BJ, UK
| | | |
Collapse
|
13
|
Li Y, Du P, Zeng H, Wei Y, Fu H, Zhong X, Ma X. Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma. PeerJ 2023; 11:e15674. [PMID: 37583914 PMCID: PMC10424667 DOI: 10.7717/peerj.15674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/11/2023] [Indexed: 08/17/2023] Open
Abstract
Objective This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging. Methods The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set. Results Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09-25.65], p < 0.001). Conclusions The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.
Collapse
Affiliation(s)
- Yueyi Li
- Department of Targeting Therapy & Immunology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Peixin Du
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hao Zeng
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuhao Wei
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Haoxuan Fu
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Xi Zhong
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xuelei Ma
- Department of Targeting Therapy & Immunology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| |
Collapse
|
14
|
Caputo A, L’Imperio V, Merolla F, Girolami I, Leoni E, Mea VD, Pagni F, Fraggetta F. The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board. Pathologica 2023; 115:127-136. [PMID: 37387439 PMCID: PMC10462988 DOI: 10.32074/1591-951x-868] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 07/01/2023] Open
Abstract
Objective The digital revolution in pathology represents an invaluable resource fto optimise costs, reduce the risk of error and improve patient care, even though it is still adopted in a minority of laboratories. Barriers include concerns about initial costs, lack of confidence in using whole slide images for primary diagnosis, and lack of guidance on transition. To address these challenges and develop a programme to facilitate the introduction of digital pathology (DP) in Italian pathology departments, a panel discussion was set up to identify the key points to be considered. Methods On 21 July 2022, an initial conference call was held on Zoom to identify the main issues to be discussed during the face-to-face meeting. The final summit was divided into four different sessions: (I) the definition of DP, (II) practical applications of DP, (III) the use of AI in DP, (IV) DP and education. Results Essential requirements for the implementation of DP are a fully tracked and automated workflow, selection of the appropriate scanner based on the specific needs of each department, and a strong commitment combined with coordinated teamwork (pathologists, technicians, biologists, IT service and industries). This could reduce human error, leading to the application of AI tools for diagnosis, prognosis and prediction. Open challenges are the lack of specific regulations for virtual slide storage and the optimal storage solution for large volumes of slides. Conclusion Teamwork is key to DP transition, including close collaboration with industry. This will ease the transition and help bridge the gap that currently exists between many labs and full digitisation. The ultimate goal is to improve patient care.
Collapse
Affiliation(s)
- Alessandro Caputo
- Department of Pathology, Ruggi University Hospital, Salerno, Italy
- Pathology Unit, Gravina Hospital Caltagirone ASP, Catania, Italy
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, University of Milan-Bicocca, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Francesco Merolla
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy; Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität
| | - Eleonora Leoni
- Pathology Unit, Busto Arsizio Hospital, Busto Arsizio, Italy
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, University of Milan-Bicocca, IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
| | | |
Collapse
|
15
|
Jiménez-Sánchez D, López-Janeiro Á, Villalba-Esparza M, Ariz M, Kadioglu E, Masetto I, Goubert V, Lozano MD, Melero I, Hardisson D, Ortiz-de-Solórzano C, de Andrea CE. Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images. NPJ Digit Med 2023; 6:48. [PMID: 36959234 PMCID: PMC10036616 DOI: 10.1038/s41746-023-00795-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 03/10/2023] [Indexed: 03/25/2023] Open
Abstract
Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83-0.95. Our model predictions resulted in concordance for 96,8% of cases (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.
Collapse
Affiliation(s)
- Daniel Jiménez-Sánchez
- Program of Solid Tumors and Biomarkers, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Álvaro López-Janeiro
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
- Department of Pathology, Hospital Universitario La Paz, IdiPAZ, Madrid, Spain
| | - María Villalba-Esparza
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain
| | - Mikel Ariz
- Program of Solid Tumors and Biomarkers, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain
| | - Ece Kadioglu
- Lunaphore Technologies SA, Tolochenaz, Switzerland
| | | | | | - Maria D Lozano
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain
- Center for Biomedical Research in the Cancer Network (CIBERONC), Madrid, Spain
| | - Ignacio Melero
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain
- Center for Biomedical Research in the Cancer Network (CIBERONC), Madrid, Spain
- Department of Immunology and Immunotherapy, Clínica Universidad de Navarra, Pamplona, Spain
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - David Hardisson
- Department of Pathology, Hospital Universitario La Paz, IdiPAZ, Madrid, Spain
- Center for Biomedical Research in the Cancer Network (CIBERONC), Madrid, Spain
- Molecular Pathology and Therapeutic Targets Group, La Paz University Hospital, IdiPAZ, Madrid, Spain
- Faculty of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
| | - Carlos Ortiz-de-Solórzano
- Program of Solid Tumors and Biomarkers, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain
- Center for Biomedical Research in the Cancer Network (CIBERONC), Madrid, Spain
| | - Carlos E de Andrea
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain.
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain.
- Center for Biomedical Research in the Cancer Network (CIBERONC), Madrid, Spain.
| |
Collapse
|
16
|
Lim MJ, Yagnik G, Henkel C, Frost SF, Bien T, Rothschild KJ. MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissues. Front Chem 2023; 11:1182404. [PMID: 37201132 PMCID: PMC10187789 DOI: 10.3389/fchem.2023.1182404] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/14/2023] [Indexed: 05/20/2023] Open
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is one of the most widely used methods for imaging the spatial distribution of unlabeled small molecules such as metabolites, lipids and drugs in tissues. Recent progress has enabled many improvements including the ability to achieve single cell spatial resolution, 3D-tissue image reconstruction, and the precise identification of different isomeric and isobaric molecules. However, MALDI-MSI of high molecular weight intact proteins in biospecimens has thus far been difficult to achieve. Conventional methods normally require in situ proteolysis and peptide mass fingerprinting, have low spatial resolution, and typically detect only the most highly abundant proteins in an untargeted manner. In addition, MSI-based multiomic and multimodal workflows are needed which can image both small molecules and intact proteins from the same tissue. Such a capability can provide a more comprehensive understanding of the vast complexity of biological systems at the organ, tissue, and cellular levels of both normal and pathological function. A recently introduced top-down spatial imaging approach known as MALDI HiPLEX-IHC (MALDI-IHC for short) provides a basis for achieving this high-information content imaging of tissues and even individual cells. Based on novel photocleavable mass-tags conjugated to antibody probes, high-plex, multimodal and multiomic MALDI-based workflows have been developed to image both small molecules and intact proteins on the same tissue sample. Dual-labeled antibody probes enable multimodal mass spectrometry and fluorescent imaging of targeted intact proteins. A similar approach using the same photocleavable mass-tags can be applied to lectin and other probes. We detail here several examples of MALDI-IHC workflows designed to enable high-plex, multiomic and multimodal imaging of tissues at a spatial resolution as low as 5 µm. This approach is compared to other existing high-plex methods such as imaging mass cytometry, MIBI-TOF, GeoMx and CODEX. Finally, future applications of MALDI-IHC are discussed.
Collapse
Affiliation(s)
- Mark J. Lim
- AmberGen, Inc., Billerica, MA, United States
- *Correspondence: Mark J. Lim, ; Kenneth J. Rothschild,
| | | | | | | | - Tanja Bien
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Kenneth J. Rothschild
- AmberGen, Inc., Billerica, MA, United States
- Department of Physics and Photonics Center, Boston University, Boston, MA, United States
- *Correspondence: Mark J. Lim, ; Kenneth J. Rothschild,
| |
Collapse
|