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Chen B, Chen C, Zhao X, Wu X. Perioperative/postoperative anxiety and its interventions in gynecological cancers: a comprehensive review of clinical evidence. Front Psychiatry 2024; 15:1383029. [PMID: 39104879 PMCID: PMC11298462 DOI: 10.3389/fpsyt.2024.1383029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 06/28/2024] [Indexed: 08/07/2024] Open
Abstract
Gynecological cancers are prevalent malignancies among females, and surgical intervention is the primary therapeutic approach offering the possibility of a definitive cure. Recent research has highlighted the susceptibility of gynecological cancer patients to experiencing anxiety symptoms during the perioperative and postoperative phases, with this psychological condition being linked to suboptimal recovery following surgery. Nevertheless, certain interventions have shown promise in mitigating perioperative and postoperative anxiety in gynecological cancer patients. In this study, we conducted a comprehensive review to collect the existing evidence on this subject. Through a systematic search across six common databases, we screened and included 28 pertinent studies. The current review emphasizes the elevated occurrence of perioperative and postoperative anxiety among patients with gynecological cancers (i.e., uterine, cervical, ovarian, endometrial, and vulval cancers). Specific nursing interventions (i.e., crisis intervention nursing, multidisciplinary collaborative continuous nursing, psychological nursing, comprehensive psychological nursing, reminiscence therapy involved care, cognitive behavioral stress management, hospital-family integrated continuation nursing, high-quality nursing care, relaxation-focused nursing program, and relaxation/counseling intervention) and psychotropic medications may serve as dependable approaches to mitigate perioperative and postoperative anxiety. This study represents a novel contribution to the literature by providing a characterization of perioperative and postoperative anxiety in the context of gynecological oncology. The findings underscore the significance of addressing perioperative and postoperative anxiety as a critical clinical concern for individuals with gynecological cancers, emphasizing the need for further research to develop effective interventions.
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Affiliation(s)
- Beibei Chen
- Department of Gynecology, Hangzhou Women’s Hospital (Hangzhou Maternity and Child Health Care Hospital), Hangzhou, Zhejiang, China
| | - Chunyan Chen
- Department of Nephrology, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing, Zhejiang, China
| | - Xiumin Zhao
- Department of Gynecology, Hangzhou Women’s Hospital (Hangzhou Maternity and Child Health Care Hospital), Hangzhou, Zhejiang, China
| | - Xiaoxia Wu
- Department of Obstetrics and Gynecology, Tiantai Pingqiao Central Health Hospital, Tiantai People's Hospital of Zhejiang Province (Tiantai Branch of Zhejiang Provincial People's Hospital), Hangzhou Medical College, Taizhou, Zhejiang, China
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Jiang X, Zhou R, Jiang F, Yan Y, Zhang Z, Wang J. Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning. Front Oncol 2024; 14:1401496. [PMID: 38812780 PMCID: PMC11133637 DOI: 10.3389/fonc.2024.1401496] [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: 03/15/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.
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Affiliation(s)
- Xin Jiang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Ruilong Zhou
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Fengle Jiang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Yanan Yan
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Zheting Zhang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Jianmin Wang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
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3
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Lin Y, Chen X, Lin L, Xu B, Zhu X, Lin X. Sesamolin serves as an MYH14 inhibitor to sensitize endometrial cancer to chemotherapy and endocrine therapy via suppressing MYH9/GSK3β/β-catenin signaling. Cell Mol Biol Lett 2024; 29:63. [PMID: 38698330 PMCID: PMC11067147 DOI: 10.1186/s11658-024-00583-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/24/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Endometrial cancer (EC) is one of the most common gynecological cancers. Herein, we aimed to define the role of specific myosin family members in EC because this protein family is involved in the progression of various cancers. METHODS Bioinformatics analyses were performed to reveal EC patients' prognosis-associated genes in patients with EC. Furthermore, colony formation, immunofluorescence, cell counting kit 8, wound healing, and transwell assays as well as coimmunoprecipitation, cycloheximide chase, luciferase reporter, and cellular thermal shift assays were performed to functionally and mechanistically analyze human EC samples, cell lines, and a mouse model, respectively. RESULTS Machine learning techniques identified MYH14, a member of the myosin family, as the prognosis-associated gene in patients with EC. Furthermore, bioinformatics analyses based on public databases showed that MYH14 was associated with EC chemoresistance. Moreover, immunohistochemistry validated MYH14 upregulation in EC cases compared with that in normal controls and confirmed that MYH14 was an independent and unfavorable prognostic indicator of EC. MYH14 impaired cell sensitivity to carboplatin, paclitaxel, and progesterone, and increased cell proliferation and metastasis in EC. The mechanistic study showed that MYH14 interacted with MYH9 and impaired GSK3β-mediated β-catenin ubiquitination and degradation, thus facilitating the Wnt/β-catenin signaling pathway and epithelial-mesenchymal transition. Sesamolin, a natural compound extracted from Sesamum indicum (L.), directly targeted MYH14 and attenuated EC progression. Additionally, the compound disrupted the interplay between MYH14 and MYH9 and repressed MYH9-regulated Wnt/β-catenin signaling. The in vivo study further verified sesamolin as a therapeutic drug without side effects. CONCLUSIONS Herein, we identified that EC prognosis-associated MYH14 was independently responsible for poor overall survival time of patients, and it augmented EC progression by activating Wnt/β-catenin signaling. Targeting MYH14 by sesamolin, a cytotoxicity-based approach, can be applied synergistically with chemotherapy and endocrine therapy to eventually mitigate EC development. This study emphasizes MYH14 as a potential target and sesamolin as a valuable natural drug for EC therapy.
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Affiliation(s)
- Yibin Lin
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Xiao Chen
- Department of Intensive Care Unit, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, China
- Department of Intensive Care Unit, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China
| | - Linping Lin
- Hunan Institute of Engineering, Xiangtan, 411100, Hunan, China
| | - Benhua Xu
- Department of Radiation Oncology, Fujian Medical University Union Hospital, Xinquan Road 29, Gulou District, Fuzhou, 350001, Fujian, China.
| | - Xiaofeng Zhu
- Department of Oral Maxillo-Facial Surgery, The First Affiliated Hospital, Fujian Medical University, No. 20 Chazhong Road, Taijing District, Fuzhou, 350005, Fujian, China.
- Department of Oral Maxillo-Facial Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
| | - Xian Lin
- Shenzhen Key Laboratory of Inflammatory and Immunology Diseases, No. 1120 Lianhua Road, Futian District, Shenzhen, 518036, Guangdong, China.
- Peking University Shenzhen Hospital, Shenzhen, 518036, Guangdong, China.
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Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385:e078378. [PMID: 38626948 PMCID: PMC11019967 DOI: 10.1136/bmj-2023-078378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Paula Dhiman
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, Netherlands
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Johannes B Reitsma
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany
| | - Jennifer Catherine Camaradou
- Patient representative, Health Data Research UK patient and public involvement and engagement group
- Patient representative, University of East Anglia, Faculty of Health Sciences, Norwich Research Park, Norwich, UK
| | - Leo Anthony Celi
- Beth Israel Deaconess Medical Center, Boston, MA, USA
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Alastair K Denniston
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | | | - Emily Lam
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Naomi Lee
- National Institute for Health and Care Excellence, London, UK
| | - Elizabeth W Loder
- The BMJ, London, UK
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- Wellcome Trust, London, UK
- Alan Turing Institute, London, UK
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children Toronto, ON, Canada
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Richard Parnell
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Sherri Rose
- Department of Health Policy and Center for Health Policy, Stanford University, Stanford, CA, USA
| | - Karandeep Singh
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Patricia Logullo
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
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Liu CL, Lee MH, Hsueh SN, Chung CC, Lin CJ, Chang PH, Luo AC, Weng HC, Lee YH, Dai MJ, Tsai MJ. A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment. Comput Biol Med 2024; 172:108244. [PMID: 38457931 DOI: 10.1016/j.compbiomed.2024.108244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Abstract
The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model's robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.
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Affiliation(s)
- Chien-Liang Liu
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC.
| | - Min-Hsuan Lee
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC
| | - Shan-Ni Hsueh
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC
| | - Chia-Chen Chung
- Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu, 30010, Taiwan, ROC
| | - Chun-Ju Lin
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Po-Han Chang
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - An-Chun Luo
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Hsuan-Chi Weng
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Yu-Hsien Lee
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Ming-Ji Dai
- Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu County, 310401, Taiwan, ROC
| | - Min-Juei Tsai
- Department of Nephrology, Chang-Hua Hospital, Ministry of Health and Welfare, Changhua, No. 80, Sec. 2, Zhongzheng Rd., Puxin Township, Changhua County, 513007, Taiwan, ROC.
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Li P, Wang S, Wan H, Huang Y, Yin K, Sun K, Jin H, Wang Z. Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion. Front Immunol 2024; 15:1258475. [PMID: 38352883 PMCID: PMC10862485 DOI: 10.3389/fimmu.2024.1258475] [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: 07/14/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024] Open
Abstract
Background Given the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity. Methods Based on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer. SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET. Results The expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells. Conclusion To summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs. C-MET was screened and validated for its tumor driver gene and immune regulation function (inducing t-cell exhaustion) in glioma.
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Affiliation(s)
- Pengping Li
- Department of Thyroid and Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Shaowen Wang
- Neuromedicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Hong Wan
- Department of General Surgery, Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuqing Huang
- Department of Thyroid and Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Kexin Yin
- Department of Thyroid and Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Ke Sun
- Department of Thyroid and Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Haigang Jin
- Department of Thyroid and Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Zhenyu Wang
- Department of Thyroid and Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
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Wang Q, Chang Z, Liu X, Wang Y, Feng C, Ping Y, Feng X. Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis. J Med Internet Res 2024; 26:e48527. [PMID: 38252469 PMCID: PMC10845031 DOI: 10.2196/48527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. OBJECTIVE This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. RESULTS A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. CONCLUSIONS Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.
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Affiliation(s)
- Qingyi Wang
- Department of First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Zhuo Chang
- Basic Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xiaofang Liu
- Department of First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yunrui Wang
- Department of First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Chuwen Feng
- Department of First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yunlu Ping
- Department of First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xiaoling Feng
- Department of Gynecology, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
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8
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Laios A, Kalampokis E, Mamalis ME, Thangavelu A, Tan YS, Hutson R, Munot S, Broadhead T, Nugent D, Theophilou G, Jackson RE, De Jong D. Explaining the Elusive Nature of a Well-Defined Threshold for Blood Transfusion in Advanced Epithelial Ovarian Cancer Cytoreductive Surgery. Diagnostics (Basel) 2023; 14:94. [PMID: 38201403 PMCID: PMC10795734 DOI: 10.3390/diagnostics14010094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/18/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. To address this, we devised a Machine Learning (ML)-driven prediction algorithm aimed at prompting and elucidating a communication alert for BT based on anticipated peri-operative events independent of existing BT policies. We analyzed data from 403 EOC patients who underwent cytoreductive surgery between 2014 and 2019. The estimated blood volume (EBV), calculated using the formula EBV = weight × 80, served for setting a 10% EBV threshold for individual intervention. Based on known estimated blood loss (EBL), we identified two distinct groups. The Receiver operating characteristic (ROC) curves revealed satisfactory results for predicting events above the established threshold (AUC 0.823, 95% CI 0.76-0.88). Operative time (OT) was the most significant factor influencing predictions. Intra-operative blood loss exceeding 10% EBV was associated with OT > 250 min, primary surgery, serous histology, performance status 0, R2 resection and surgical complexity score > 4. Certain sub-procedures including large bowel resection, stoma formation, ileocecal resection/right hemicolectomy, mesenteric resection, bladder and upper abdominal peritonectomy demonstrated clear associations with an elevated interventional risk. Our findings emphasize the importance of obtaining a rough estimate of OT in advance for precise prediction of blood requirements.
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Affiliation(s)
- Alexandros Laios
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | - Evangelos Kalampokis
- Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece; (E.K.); (M.-E.M.)
- Center for Research & Technology HELLAS (CERTH), 6th km Charilaou-Thermi Rd, 57001 Thessaloniki, Greece
| | - Marios-Evangelos Mamalis
- Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece; (E.K.); (M.-E.M.)
| | - Amudha Thangavelu
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | - Yong Sheng Tan
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | - Richard Hutson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | - Sarika Munot
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | - Tim Broadhead
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | - David Nugent
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | - Georgios Theophilou
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
| | | | - Diederick De Jong
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (A.T.); (Y.S.T.); (R.H.); (S.M.); (T.B.); (D.N.); (G.T.); (D.D.J.)
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9
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Yang Z, Zhou D, Huang J. Identifying Explainable Machine Learning Models and a Novel SFRP2 + Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer. Int J Mol Sci 2023; 24:16942. [PMID: 38069266 PMCID: PMC10706905 DOI: 10.3390/ijms242316942] [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: 09/25/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.
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Affiliation(s)
| | | | - Jun Huang
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
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10
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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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