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Caranfil E, Lami K, Uegami W, Fukuoka J. Artificial Intelligence and Lung Pathology. Adv Anat Pathol 2024; 31:344-351. [PMID: 38780094 DOI: 10.1097/pap.0000000000000448] [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: 05/25/2024]
Abstract
This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.
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Affiliation(s)
- Emanuel Caranfil
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Kris Lami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
| | - Wataru Uegami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
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Hempenius MA, Koomen BM, Deckers IAG, Oosting SF, Willems SM, van der Vegt B. Considerable interlaboratory variation in PD-L1 positivity for head and neck squamous cell carcinoma in the Netherlands- A nationwide evaluation study. Histopathology 2024; 85:133-142. [PMID: 38606992 DOI: 10.1111/his.15184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/11/2024] [Accepted: 03/16/2024] [Indexed: 04/13/2024]
Abstract
AIMS Patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC) are eligible for first-line immune checkpoint inhibition if their tumour is positive for programmed death ligand 1 (PD-L1) determined by the combined positive score (CPS). This nationwide study, using real-world data, investigated the developing PD-L1 testing landscape in the first 3 years after introduction of the test in HNSCC and examined interlaboratory variation in PD-L1 positivity rates. METHODS Pathology reports of HNSCC patients mentioning PD-L1 were extracted from the Dutch Pathology Registry (Palga). Tumour and PD-L1 testing characteristics were analysed per year and interlaboratory variation in PD-L1 positivity rates was assessed using funnel plots with 95% confidence limits around the overall mean. RESULTS A total of 817 PD-L1 tests were reported in 702 patients among 19 laboratories; 85.2% of the tests on histological material were stated to be positive. The national PD-L1 positivity rate differed significantly per year during the study period (79.7-89.9%). The use of the recommended 22C3 antibody increased from 59.9 to 74.3%. A total of 673 PD-L1 tests on histological material from 12 laboratories were analysed to investigate interlaboratory variation. Four (33%) deviated significantly from the national mean of PD-L1-positive cases using CPS ≥ 1 cut-off, while two (17%) deviated significantly for CPS ≥ 20 cut-off. CONCLUSION In the first 3 years of PD-L1 assessment in HNSCC, the testing landscape became more uniform. However, interlaboratory variation in PD-L1 positivity rates between Dutch laboratories was substantial. This implies that there is a need for further test standardisation to reduce this variation.
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Affiliation(s)
- Maaike Anna Hempenius
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bregje M Koomen
- Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | - Sjoukje F Oosting
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan M Willems
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bert van der Vegt
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Lee KS, Choi E, Cho SI, Park S, Ryu J, Puche AV, Ma M, Park J, Jung W, Ro J, Kim S, Park G, Song S, Ock CY, Choe G, Park JH. An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability. Histopathology 2024; 85:81-91. [PMID: 38477366 DOI: 10.1111/his.15176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/20/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024]
Abstract
AIMS Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.
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Affiliation(s)
- Kyu Sang Lee
- Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Euno Choi
- Department of Pathology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | | | | | | | | | | | | | | | | | | | | | | | | | - Gheeyoung Choe
- Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Jeong Hwan Park
- Department of Pathology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
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Cho H, Moon D, Heo SM, Chu J, Bae H, Choi S, Lee Y, Kim D, Jo Y, Kim K, Hwang K, Lee D, Choi HK, Kim S. Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy. NPJ Precis Oncol 2024; 8:131. [PMID: 38877301 PMCID: PMC11178780 DOI: 10.1038/s41698-024-00621-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: 01/18/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024] Open
Abstract
There has been a persistent demand for an innovative modality in real-time histologic imaging, distinct from the conventional frozen section technique. We developed an artificial intelligence-driven real-time evaluation model for gastric cancer tissue using confocal laser endomicroscopic system. The remarkable performance of the model suggests its potential utilization as a standalone modality for instantaneous histologic assessment and as a complementary tool for pathologists' interpretation.
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Affiliation(s)
- Haeyon Cho
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Damin Moon
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea
| | - So Mi Heo
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jinah Chu
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyunsik Bae
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Pathology center, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Sangjoon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yubin Lee
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea
| | - Dongmin Kim
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea
| | - Yeonju Jo
- VPIX Medical Inc., Daejeon, Republic of Korea
| | | | | | - Dakeun Lee
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
| | - Heung-Kook Choi
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea.
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
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Dixon D, Sattar H, Moros N, Kesireddy SR, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024; 16:e59954. [PMID: 38854327 PMCID: PMC11161909 DOI: 10.7759/cureus.59954] [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] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
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Affiliation(s)
- Diny Dixon
- Medicine, Jubilee Mission Medical College and Research Institute, Thrissur, IND
| | - Hina Sattar
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Natalia Moros
- Medicine, Pontifical Javeriana University Medical School, Bogotá, COL
| | | | - Huma Ahsan
- Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
| | | | - Madiha Fatima
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Dhruvi Doshi
- Medicine, Gujarat Cancer Society Medical College, Hospital & Research Centre, Ahmedabad, IND
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Kim H, Kim S, Choi S, Park C, Park S, Pereira S, Ma M, Yoo D, Paeng K, Jung W, Park S, Ock CY, Lee SH, Choi YL, Chung JH. Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer. JCO Precis Oncol 2024; 8:e2300556. [PMID: 38723233 DOI: 10.1200/po.23.00556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/11/2023] [Accepted: 04/03/2024] [Indexed: 05/15/2024] Open
Abstract
PURPOSE Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS The AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS. RESULTS In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS <1% group better than the pathologist's reading, with the TPS ≥50% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS <1% group). CONCLUSION PD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.
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Affiliation(s)
- Hyojin Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Changhee Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | | | | | - Minuk Ma
- Lunit Inc., Seoul, Republic of Korea
| | | | | | | | - Sehhoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yoon-La Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jin-Haeng Chung
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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van Eekelen L, Spronck J, Looijen-Salamon M, Vos S, Munari E, Girolami I, Eccher A, Acs B, Boyaci C, de Souza GS, Demirel-Andishmand M, Meesters LD, Zegers D, van der Woude L, Theelen W, van den Heuvel M, Grünberg K, van Ginneken B, van der Laak J, Ciompi F. Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images. Sci Rep 2024; 14:7136. [PMID: 38531958 DOI: 10.1038/s41598-024-57067-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26-0.81, mean reader-AI kappa = 0.49, 95% CI 0.27-0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.
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Affiliation(s)
- Leander van Eekelen
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Joey Spronck
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Monika Looijen-Salamon
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Shoko Vos
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Enrico Munari
- Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Balazs Acs
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Ceren Boyaci
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Gabriel Silva de Souza
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Muradije Demirel-Andishmand
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Luca Dulce Meesters
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Daan Zegers
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Lieke van der Woude
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Willemijn Theelen
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Michel van den Heuvel
- Respiratory Diseases Department, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Katrien Grünberg
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, P.O.Box 9101, 6500 HB, Nijmegen, The Netherlands
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Cho U, Im S, Park HS. Exploring histological predictive biomarkers for immune checkpoint inhibitor therapy response in non-small cell lung cancer. J Pathol Transl Med 2024; 58:49-58. [PMID: 38389279 PMCID: PMC10948248 DOI: 10.4132/jptm.2024.01.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/16/2024] [Accepted: 01/29/2024] [Indexed: 02/24/2024] Open
Abstract
Treatment challenges persist in advanced lung cancer despite the development of therapies beyond the traditional platinum-based chemotherapy. The early 2000s marked a shift to tyrosine kinase inhibitors targeting epidermal growth factor receptor, ushering in personalized genetic-based treatment. A further significant advance was the development of immune checkpoint inhibitors (ICIs), especially for non-small cell lung cancer. These target programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4, which enhanced the immune response against tumor cells. However, not all patients respond, and immune-related toxicities arise. This review emphasizes identifying biomarkers for ICI response prediction. While PD-L1 is a widely used, validated biomarker, its predictive accuracy is imperfect. Investigating tumor-infiltrating lymphocytes, tertiary lymphoid structure, and emerging biomarkers such as high endothelial venule, Human leukocyte antigen class I, T-cell immunoreceptors with Ig and ITIM domains, and lymphocyte activation gene-3 counts is promising. Understanding and exploring additional predictive biomarkers for ICI response are crucial for enhancing patient stratification and overall care in lung cancer treatment.
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Affiliation(s)
- Uiju Cho
- Department of Pathology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
| | - Soyoung Im
- Department of Pathology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
| | - Hyung Soon Park
- Division of Medical Oncology, Department of Internal Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
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Jung M, Song SG, Cho SI, Shin S, Lee T, Jung W, Lee H, Park J, Song S, Park G, Song H, Park S, Lee J, Kang M, Park J, Pereira S, Yoo D, Chung K, Ali SM, Kim SW. Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases. Breast Cancer Res 2024; 26:31. [PMID: 38395930 PMCID: PMC10885430 DOI: 10.1186/s13058-024-01784-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/11/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.
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Affiliation(s)
- Minsun Jung
- Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung Geun Song
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - So-Woon Kim
- Department of Pathology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
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Shin S, Moon J, Oum C, Kim S, Cho SI, Lim Y, Ock CY, Shin S. Discontinuation risk from adverse events: immunotherapy alone vs. combined with chemotherapy: a systematic review and network meta-analysis. BMC Cancer 2024; 24:152. [PMID: 38291376 PMCID: PMC10825980 DOI: 10.1186/s12885-024-11897-4] [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: 10/11/2023] [Accepted: 01/19/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND While immunotherapy combined with chemotherapy (Chemo-IO) is generally recognized for providing superior outcomes compared to monotherapy (mono-IO), it is associated with a higher incidence of treatment-related adverse events (TRAEs), which may lead to treatment discontinuation. In this study, we compared the rates of treatment discontinuation between mono-IO and Chemo-IO as first-line treatments for various solid tumors. METHODS We systematically reviewed clinical trials from databases (PubMed, Embase, Cochrane Library, and an additional source) published from January 1, 2018, to July 10, 2023. We included phase III randomized controlled trials (RCTs) that utilized immunotherapy agents in at least one arm as first-line treatments for a variety of solid tumors. Data extraction followed the Preferred Reporting Items for Systematic Reviews (PRISMA) extension statement for network meta-analysis. A random effects model was used for the network meta-analysis, with the risk of bias assessed using the Cochrane risk-of-bias tool II. The primary outcomes encompassed treatment discontinuation rates due to TRAEs among patients who underwent immunotherapy, either alone or combined with chemotherapy, for various solid tumors. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated to compare between treatment groups. RESULTS From 29 RCTs, a total of 21,677 patients and 5 types of treatment were analyzed. Compared to mono-IO, Chemo-IO showed a significantly higher rate of discontinuation due to TRAEs (RR 2.68, 95% CI 1.98-3.63). Subgroup analysis for non-small cell lung cancer (NSCLC) patients also exhibited a greater risk of discontinuation due to TRAEs with Chemo-IO compared to mono-IO (RR 2.93, 95% CI 1.67-5.14). Additional analyses evaluating discontinuation rates due to either treatment emergent adverse events (TEAEs) or AEs regardless of causality (any AEs) consistently revealed an elevated risk associated with Chemo-IO. CONCLUSIONS Chemo-IO was associated with an elevated risk of treatment discontinuation not only due to TRAEs but also any AEs or TEAEs. Given that the treatment duration can impact clinical outcomes, a subset of patients might benefit more from mono-IO than combination therapy. Further research is imperative to identify and characterize this subset.
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Affiliation(s)
- Sangwon Shin
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea
| | - Jimin Moon
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea
| | - Chiyoon Oum
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea
| | - Seulki Kim
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea
| | - Soo Ick Cho
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea
| | - Yoojoo Lim
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea
| | - Chan-Young Ock
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea
| | - Seunghwan Shin
- Lunit, 4th to 8th floors, 374, Gangnam-daero, Gangnam-gu, Seoul, Republic of Korea.
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Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S, Favali M, Spagnoletti A, Castelo-Branco L, Dolezal J, Pearson AT, Lo Russo G, Proto C, Ganzinelli M, Giani C, Ambrosini E, Turajlic S, Au L, Koopman M, Delaloge S, Kather JN, de Braud F, Garassino MC, Pentheroudakis G, Spencer C, Pedrocchi ALG. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol 2024; 35:29-65. [PMID: 37879443 DOI: 10.1016/j.annonc.2023.10.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
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Affiliation(s)
- A Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.
| | - V Miskovic
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - M Zanitti
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - F Trovo
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - C Genova
- UO Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genoa; Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa
| | - G Viscardi
- Precision Medicine Department, Università degli Studi della Campania Luigi Vanvitelli, Naples
| | - S E Rebuzzi
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa; Medical Oncology Unit, Ospedale San Paolo, Savona, Italy
| | - L Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - L Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - S Kosta
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - M Favali
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - A Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - L Castelo-Branco
- ESMO European Society for Medical Oncology, Lugano, Switzerland; NOVA National School of Public Health, Lisboa, Portugal
| | - J Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - A T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - G Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - E Ambrosini
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - S Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London
| | - L Au
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne; Sir Peter MacCallum Department of Medical Oncology, The University of Melbourne, Melbourne, Australia
| | - M Koopman
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - S Delaloge
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - J N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - F de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M C Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | | | - C Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London.
| | - A L G Pedrocchi
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
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12
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Shao J, Feng J, Li J, Liang S, Li W, Wang C. Novel tools for early diagnosis and precision treatment based on artificial intelligence. CHINESE MEDICAL JOURNAL PULMONARY AND CRITICAL CARE MEDICINE 2023; 1:148-160. [PMID: 39171128 PMCID: PMC11332840 DOI: 10.1016/j.pccm.2023.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Indexed: 08/23/2024]
Abstract
Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate. Chest computed tomography (CT) serves as an essential tool for lung cancer screening, and pathology images are the gold standard for lung cancer diagnosis. However, medical image evaluation relies on manual labor and suffers from missed diagnosis or misdiagnosis, and physician heterogeneity. The rapid development of artificial intelligence (AI) has brought a whole novel opportunity for medical task processing, demonstrating the potential for clinical application in lung cancer diagnosis and treatment. AI technologies, including machine learning and deep learning, have been deployed extensively for lung nodule detection, benign and malignant classification, and subtype identification based on CT images. Furthermore, AI plays a role in the non-invasive prediction of genetic mutations and molecular status to provide the optimal treatment regimen, and applies to the assessment of therapeutic efficacy and prognosis of lung cancer patients, enabling precision medicine to become a reality. Meanwhile, histology-based AI models assist pathologists in typing, molecular characterization, and prognosis prediction to enhance the efficiency of diagnosis and treatment. However, the leap to extensive clinical application still faces various challenges, such as data sharing, standardized label acquisition, clinical application regulation, and multimodal integration. Nevertheless, AI holds promising potential in the field of lung cancer to improve cancer care.
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Affiliation(s)
- Jun Shao
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiaming Feng
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jingwei Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shufan Liang
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Choi S, Cho SI, Jung W, Lee T, Choi SJ, Song S, Park G, Park S, Ma M, Pereira S, Yoo D, Shin S, Ock CY, Kim S. Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer. NPJ Breast Cancer 2023; 9:71. [PMID: 37648694 PMCID: PMC10469174 DOI: 10.1038/s41523-023-00577-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/17/2023] [Indexed: 09/01/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | | | | | - Su Jin Choi
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | | | | | | | - Minuk Ma
- Lunit Inc, Seoul, Republic of Korea
| | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
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14
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Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, Batistatou A. Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers (Basel) 2023; 15:3981. [PMID: 37568797 PMCID: PMC10417369 DOI: 10.3390/cancers15153981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
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Affiliation(s)
- Athena Davri
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
| | - Effrosyni Birbas
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Theofilos Kanavos
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Georgios Ntritsos
- Department of Hygiene and Epidemiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Anna Batistatou
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
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Kim M, Lee JL, Shin SJ, Bae WK, Lee HJ, Byun JH, Choi YJ, Youk J, Ock CY, Kim S, Song H, Park KH, Keam B. Phase II study of a trastuzumab biosimilar in combination with paclitaxel for HER2-positive recurrent or metastatic urothelial carcinoma: KCSG GU18-18. ESMO Open 2023; 8:101588. [PMID: 37385153 PMCID: PMC10485395 DOI: 10.1016/j.esmoop.2023.101588] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/11/2023] [Accepted: 05/21/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Human epidermal growth factor receptor 2 (HER2) is a widely explored therapeutic target in solid tumors. We evaluated the efficacy and safety of trastuzumab-pkrb, a biosimilar of trastuzumab, in combination with paclitaxel, in HER2-positive recurrent or metastatic urothelial carcinoma (UC). PATIENTS AND METHODS We enrolled 27 patients; they were administered a loading dose of 8 mg/kg trastuzumab-pkrb on day 1, followed by 6 mg/kg and 175 mg/m2 paclitaxel on day 1 every 3 weeks, intravenously. All patients received six cycles of the combination treatment and continued to receive trastuzumab-pkrb maintenance until disease progression, unacceptable toxicity, or for up to 2 years. HER2 positivity (based on immunohistochemistry analysis) was determined according to the 2013 American Society of Clinical Oncology /College of American Pathologists HER2 testing guidelines. The primary endpoint was objective response rate (ORR); the secondary endpoints were overall survival (OS), progression-free survival (PFS), and safety. RESULTS Twenty-six patients were evaluated via primary endpoint analysis. The ORR was 48.1% (1 complete and 12 partial responses) and the duration of response was 6.9 months [95% confidence interval (CI) 4.4-9.3 months]. With a median follow-up of 10.5 months, the median PFS and OS were 8.4 months (95% CI 6.2-8.8 months) and 13.5 months (95% CI 9.8 months-not reached), respectively. The most common treatment-related adverse event (TRAE) of any grade was peripheral neuropathy (88.9%). The most common grade 3/4 TRAEs were neutropenia (25.9%), thrombocytopenia (7.4%), and anemia (7.4%). CONCLUSIONS Trastuzumab-pkrb plus paclitaxel demonstrates promising efficacy with manageable toxicity profiles in patients with HER2-positive recurrent or metastatic UC.
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Affiliation(s)
- M Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul; Cancer Research Institute, Seoul National University College of Medicine, Seoul
| | - J L Lee
- Department of Oncology and Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul
| | - S J Shin
- Division of Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul
| | - W K Bae
- Department of Hemato-Oncology, Chonnam National University Medical School & Hwasun Hospital, Hwasun
| | - H J Lee
- Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon
| | - J H Byun
- Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon
| | - Y J Choi
- Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul
| | - J Youk
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul; Cancer Research Institute, Seoul National University College of Medicine, Seoul
| | - C Y Ock
- Lunit, Seoul, Republic of Korea
| | - S Kim
- Lunit, Seoul, Republic of Korea
| | - H Song
- Lunit, Seoul, Republic of Korea
| | - K H Park
- Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul
| | - B Keam
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul; Cancer Research Institute, Seoul National University College of Medicine, Seoul.
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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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Kim DH, Sun S, Cho SI, Kong HJ, Lee JW, Lee JH, Suh DH. Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists. Am J Clin Dermatol 2023:10.1007/s40257-023-00777-5. [PMID: 37160644 DOI: 10.1007/s40257-023-00777-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
BACKGROUND Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. OBJECTIVES We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test. METHODS A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions. RESULTS In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson's correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation. CONCLUSIONS Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity.
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Affiliation(s)
- Dong Hyo Kim
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea
| | - Sukkyu Sun
- Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea
| | - Soo Ick Cho
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyoun-Joong Kong
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Ji Won Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
| | - Jun Hyo Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea
| | - Dae Hun Suh
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea.
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea.
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18
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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Xie J, Luo X, Deng X, Tang Y, Tian W, Cheng H, Zhang J, Zou Y, Guo Z, Xie X. Advances in artificial intelligence to predict cancer immunotherapy efficacy. Front Immunol 2023; 13:1076883. [PMID: 36685496 PMCID: PMC9845588 DOI: 10.3389/fimmu.2022.1076883] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/09/2022] [Indexed: 01/05/2023] Open
Abstract
Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.
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Affiliation(s)
- Jindong Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiyuan Luo
- School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xinpei Deng
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuhui Tang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Wenwen Tian
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hui Cheng
- School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Junsheng Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yutian Zou
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
| | - Zhixing Guo
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
| | - Xiaoming Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,*Correspondence: Xiaoming Xie, ; Zhixing Guo, ; Yutian Zou,
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Juhas M. Artificial Intelligence in Microbiology. BRIEF LESSONS IN MICROBIOLOGY 2023:93-109. [DOI: 10.1007/978-3-031-29544-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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21
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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22
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Abbasian MH, Ardekani AM, Sobhani N, Roudi R. The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment. Cancers (Basel) 2022; 14:5144. [PMID: 36291929 PMCID: PMC9600051 DOI: 10.3390/cancers14205144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/18/2022] [Indexed: 08/17/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.
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Affiliation(s)
- Mohammad Hadi Abbasian
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Ali M. Ardekani
- Department of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Navid Sobhani
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Raheleh Roudi
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA 94305, USA
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23
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Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms. Diagnostics (Basel) 2022; 12:diagnostics12102340. [PMID: 36292028 PMCID: PMC9600129 DOI: 10.3390/diagnostics12102340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 12/03/2022] Open
Abstract
Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.
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Yin X, Liao H, Yun H, Lin N, Li S, Xiang Y, Ma X. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin Cancer Biol 2022; 86:146-159. [PMID: 35963564 DOI: 10.1016/j.semcancer.2022.08.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/26/2022]
Abstract
Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.
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Affiliation(s)
- Xiaomeng Yin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hong Yun
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Nan Lin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Shen Li
- West China School of Medicine, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Yu Xiang
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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