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O'Sullivan NJ, Temperley HC, Horan MT, Kamran W, Corr A, O'Gorman C, Saadeh F, Meaney JM, Kelly ME. Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04330-8. [PMID: 38744703 DOI: 10.1007/s00261-024-04330-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/16/2024]
Abstract
Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.
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Affiliation(s)
- Niall J O'Sullivan
- Department of Radiology, St. James's Hospital, Dublin, Ireland.
- School of Medicine, Trinity College Dublin, Dublin, Ireland.
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland.
| | | | - Michelle T Horan
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Waseem Kamran
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - Alison Corr
- Department of Radiology, St. James's Hospital, Dublin, Ireland
| | | | - Feras Saadeh
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - James M Meaney
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Michael E Kelly
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- Department of Surgery, St. James's Hospital, Dublin, Ireland
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He D, Zhang X, Chang Z, Liu Z, Li B. Survival time prediction in patients with high-grade serous ovarian cancer based on 18F-FDG PET/CT- derived inter-tumor heterogeneity metrics. BMC Cancer 2024; 24:337. [PMID: 38475819 DOI: 10.1186/s12885-024-12087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/05/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND The presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative 18F-FDG PET/CT derived inter-tumor heterogeneity metrics in determining progression-free survival (PFS) and overall survival (OS) in patients diagnosed with high-grade serous ovarian cancer (HGSOC). Additionally, the study investigated the potential correlation between model risk scores and the expression levels of p53 and Ki-67. METHODS A total of 292 patients diagnosed with HGSOC were retrospectively enrolled at Shengjing Hospital of China Medical University (median age: 54 ± 9.4 years). Quantitative inter-tumor heterogeneity metrics were calculated based on conventional measurements and texture features of primary and metastatic lesions in 18F-FDG PET/CT. Conventional models, heterogeneity models, and integrated models were then constructed to predict PFS and OS. Spearman's correlation coefficient (ρ) was used to evaluate the correlation between immunohistochemical scores of p53 and Ki-67 and model risk scores. RESULTS The C-indices of the integrated models were the highest for both PFS and OS models. The C-indices of the training set and testing set of the integrated PFS model were 0.898 (95% confidence interval [CI]: 0.881-0.914) and 0.891 (95% CI: 0.860-0.921), respectively. For the integrated OS model, the C-indices of the training set and testing set were 0.894 (95% CI: 0.871-0.917) and 0.905 (95% CI: 0.873-0.936), respectively. The integrated PFS model showed the strongest correlation with the expression levels of p53 (ρ = 0.859, p < 0.001) and Ki-67 (ρ = 0.829, p < 0.001). CONCLUSIONS The models based on 18F-FDG PET/CT quantitative inter-tumor heterogeneity metrics exhibited good performance for predicting the PFS and OS of patients with HGSOC. p53 and Ki-67 expression levels were strongly correlated with the risk scores of the integrated predictive models.
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Affiliation(s)
- Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Xin Zhang
- Department of General Surgery, Shengjing Hospital of China Medical University, 110004, Shenyang, P.R. China
| | - Zhihui Chang
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, Liaoning, 110004, P.R. China
| | - Zhaoyu Liu
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, Liaoning, 110004, P.R. China
| | - Beibei Li
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, Liaoning, 110004, P.R. China.
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Gan L, Li Y. Clinical Efficacy and Mechanism of Vitamin D2 in Treating Hashimoto's Thyroiditis. J Inflamm Res 2024; 17:1193-1210. [PMID: 38410421 PMCID: PMC10896103 DOI: 10.2147/jir.s441120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/30/2024] [Indexed: 02/28/2024] Open
Abstract
Objective Hashimoto's thyroiditis (HT) is one of the most common autoimmune diseases, with the highest incidence rate among autoimmune thyroid disorders. Vitamin D2 may have therapeutic effects on HT. This study aimed to elucidate the molecular mechanisms underlying vitamin D2 therapy for HT. Methods Differentially expressed genes (DEGs) associated with vitamin D2-treated HT were identified, and the DEG-associated gene enrichment pathway was explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The correlation between the hub genes and infiltrating immune cells was investigated, and the interactions among the hub genes and target drug and competing endogenous RNA (ceRNA; long non-coding RNA [lncRNA]-microRNA [miRNA]-messenger RNA [mRNA]) regulatory networks were determined. Results GO and KEGG enrichment analyses identified a total of 102 DEGs (6 upregulated and 96 downregulated) in the vitamin D2-treated group samples. The area under the curve values of the identified 10 hub genes was as follows: CCR1(0.920), CXCL1 (0.960), CXCL8 (0.960), EGR1 (0.960), FCGR3B (0.920), FOS (1.000), FPR1 (0.840), MMP9 (0.720), PTGS2 (0.960), and TREM1 (1.000). The immune enrichment scores of the mast cell (P = 0.008), neutrophil (P = 0.016), and plasmacytoid dendritic cell (P = 0.016) were significantly decreased in the vitamin D2-treated group (P < 0.05). The hub gene/drug regulatory network included 8 hub genes, 108 molecular drugs, and 114 interaction relationship pairs. The ceRNA regulatory network included 129 lncRNAs, 145 miRNAs, mRNAs (hub genes), and 324 interaction relationship pairs. Conclusion Vitamin D2 may play an immunomodulatory role by regulating the aforementioned immune-related molecules and immune cells, thereby improving its therapeutic effects on HT.
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Affiliation(s)
- Lu Gan
- Department of Endocrinology, General Hospital of Ningxia Medical University, Yinchuan City, 750000, People's Republic of China
| | - Yuqi Li
- Department of Endocrinology, General Hospital of Ningxia Medical University, Yinchuan City, 750000, People's Republic of China
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Adusumilli P, Ravikumar N, Hall G, Swift S, Orsi N, Scarsbrook A. Radiomics in the evaluation of ovarian masses - a systematic review. Insights Imaging 2023; 14:165. [PMID: 37782375 PMCID: PMC10545652 DOI: 10.1186/s13244-023-01500-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 08/12/2023] [Indexed: 10/03/2023] Open
Abstract
OBJECTIVES The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions. METHODS MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology. RESULTS After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was - 16.7%. CONCLUSION Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation. CLINICAL RELEVANCE STATEMENT Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation. KEY POINTS • Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. • Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. • Modelling with larger cohorts and real-world evaluation is required before clinical translation.
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Affiliation(s)
- Pratik Adusumilli
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
- West Yorkshire Radiology Academy, Level B Clarendon Wing, Leeds General Infirmary, Great George Street, Leeds, LS1 3EX, UK.
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, University of Leeds, Leeds, UK
| | - Geoff Hall
- Department of Medical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - Sarah Swift
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Nicolas Orsi
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
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Hatamikia S, Nougaret S, Panico C, Avesani G, Nero C, Boldrini L, Sala E, Woitek R. Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers. Eur Radiol Exp 2023; 7:50. [PMID: 37700218 PMCID: PMC10497482 DOI: 10.1186/s41747-023-00364-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/19/2023] [Indexed: 09/14/2023] Open
Abstract
High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models.Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks.Key points• This review presents studies using multiomics and artificial intelligence in ovarian cancer.• Current literature proves that integrative multiomics outperform models using single data types.• Around 60% of studies used a combination of imaging with clinical data.• The combination of genomics and transcriptomics with imaging data was infrequently used.
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Affiliation(s)
- Sepideh Hatamikia
- Research Center for Medical Image Analysis and AI (MIAAI), Danube Private University, Krems, Austria.
- Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria.
| | - Stephanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, University of Montpellier, Montpellier, France
| | - Camilla Panico
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giacomo Avesani
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Camilla Nero
- Scienze Della Salute Della Donna, del bambino e Di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Evis Sala
- Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Ramona Woitek
- Research Center for Medical Image Analysis and AI (MIAAI), Danube Private University, Krems, Austria
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
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Li H, Cai S, Deng L, Xiao Z, Guo Q, Qiang J, Gong J, Gu Y, Liu Z. Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram. Eur Radiol 2023; 33:5298-5308. [PMID: 36995415 DOI: 10.1007/s00330-023-09552-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVE This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.
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Affiliation(s)
- Haiming Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Songqi Cai
- Department of Radiology, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
| | - Lin Deng
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Zebin Xiao
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qinhao Guo
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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Huang ML, Ren J, Jin ZY, Liu XY, He YL, Li Y, Xue HD. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights Imaging 2023; 14:117. [PMID: 37395888 DOI: 10.1186/s13244-023-01464-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/11/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVES We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. METHODS Original articles investigating radiomics in OC published in PubMed, Embase, Web of Science, and the Cochrane Library between January 1, 2002, and January 6, 2023, were extracted. The methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses were performed to compare the methodological quality, baseline information, and performance metrics. Additional meta-analyses of studies exploring differential diagnoses and prognostic prediction in patients with OC were performed separately. RESULTS Fifty-seven studies encompassing 11,693 patients were included. The mean RQS was 30.7% (range - 4 to 22); less than 25% of studies had a high risk of bias and applicability concerns in each domain of QUADAS-2. A high RQS was significantly associated with a low QUADAS-2 risk and recent publication year. Significantly higher performance metrics were observed in studies examining differential diagnosis; 16 such studies as well as 13 exploring prognostic prediction were included in a separate meta-analysis, which revealed diagnostic odds ratios of 25.76 (95% confidence interval (CI) 13.50-49.13) and 12.55 (95% CI 8.38-18.77), respectively. CONCLUSION Current evidence suggests that the methodological quality of OC-related radiomics studies is unsatisfactory. Radiomics analysis based on CT and MRI showed promising results in terms of differential diagnosis and prognostic prediction. CRITICAL RELEVANCE STATEMENT Radiomics analysis has potential clinical utility; however, shortcomings persist in existing studies in terms of reproducibility. We suggest that future radiomics studies should be more standardized to better bridge the gap between concepts and clinical applications.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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Panico C, Avesani G, Zormpas-Petridis K, Rundo L, Nero C, Sala E. Radiomics and Radiogenomics of Ovarian Cancer. Radiol Clin North Am 2023; 61:749-760. [PMID: 37169435 DOI: 10.1016/j.rcl.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer.
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Sheehy J, Rutledge H, Acharya UR, Loh HW, Gururajan R, Tao X, Zhou X, Li Y, Gurney T, Kondalsamy-Chennakesavan S. Gynecological cancer prognosis using machine learning techniques: A systematic review of last three decades (1990–2022). Artif Intell Med 2023; 139:102536. [PMID: 37100507 DOI: 10.1016/j.artmed.2023.102536] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023]
Abstract
OBJECTIVE Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs. METHODS Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly. RESULTS Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes. CONCLUSION There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.
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Cuccu I, D’Oria O, Sgamba L, De Angelis E, Golia D’Augè T, Turetta C, Di Dio C, Scudo M, Bogani G, Di Donato V, Palaia I, Perniola G, Tomao F, Muzii L, Giannini A. Role of Genomic and Molecular Biology in the Modulation of the Treatment of Endometrial Cancer: Narrative Review and Perspectives. Healthcare (Basel) 2023; 11:healthcare11040571. [PMID: 36833105 PMCID: PMC9957190 DOI: 10.3390/healthcare11040571] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Endometrial cancer (EC) is one of the most common gynecological malignancies in Western countries. Traditionally, loco-reginal dissemination and histological characteristics are the main prognostic factors. Nowadays, molecular and genomic profiling showed exciting results in terms of prognostication. According to the data provided by The Cancer Genome Atlas and other studies, molecular and genomic profiling might be useful in identifying patients al low, intermediate, and high risk of recurrence. However, data regarding the therapeutic value are scant. Several prospective studies are ongoing to identify the most appropriate adjuvant strategy in EC patients, especially for those with positive nodes and low volume disease. The molecular classification has offered the possibility to improve the risk stratification and management of EC. The aim of this review is to focus on the evolution of molecular classification in EC and its impact on the research approach and on clinical management. Molecular and genomic profiling might be useful to tailor the most appropriate adjuvant strategies in apparent early-stage EC.
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Affiliation(s)
- Ilaria Cuccu
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Ottavia D’Oria
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
- Department of Medical and Surgical Sciences and Translational Medicine, Translational Medicine and Oncology, Sapienza University, 00161 Rome, Italy
- Correspondence:
| | - Ludovica Sgamba
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Emanuele De Angelis
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Tullio Golia D’Augè
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Camilla Turetta
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Camilla Di Dio
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Maria Scudo
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Giorgio Bogani
- Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
| | - Violante Di Donato
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Innocenza Palaia
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Giorgia Perniola
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Federica Tomao
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Ludovico Muzii
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Andrea Giannini
- Department of Gynecological, Obstetrical and Urological Sciences, Sapienza University of Rome, 00161 Rome, Italy
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11
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Funingana IG, Bedia JS, Huang YW, Delgado Gonzalez A, Donoso K, Gonzalez VD, Brenton JD, Ashworth A, Fantl WJ. Multiparameter single-cell proteomic technologies give new insights into the biology of ovarian tumors. Semin Immunopathol 2023; 45:43-59. [PMID: 36635516 DOI: 10.1007/s00281-022-00979-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/11/2022] [Indexed: 01/13/2023]
Abstract
High-grade serous ovarian cancer (HGSOC) is the most lethal gynecological malignancy. Its diagnosis at advanced stage compounded with its excessive genomic and cellular heterogeneity make curative treatment challenging. Two critical therapeutic challenges to overcome are carboplatin resistance and lack of response to immunotherapy. Carboplatin resistance results from diverse cell autonomous mechanisms which operate in different combinations within and across tumors. The lack of response to immunotherapy is highly likely to be related to an immunosuppressive HGSOC tumor microenvironment which overrides any clinical benefit. Results from a number of studies, mainly using transcriptomics, indicate that the immune tumor microenvironment (iTME) plays a role in carboplatin response. However, in patients receiving treatment, the exact mechanistic details are unclear. During the past decade, multiplex single-cell proteomic technologies have come to the forefront of biomedical research. Mass cytometry or cytometry by time-of-flight, measures up to 60 parameters in single cells that are in suspension. Multiplex cellular imaging technologies allow simultaneous measurement of up to 60 proteins in single cells with spatial resolution and interrogation of cell-cell interactions. This review suggests that functional interplay between cell autonomous responses to carboplatin and the HGSOC immune tumor microenvironment could be clarified through the application of multiplex single-cell proteomic technologies. We conclude that for better clinical care, multiplex single-cell proteomic technologies could be an integral component of multimodal biomarker development that also includes genomics and radiomics. Collection of matched samples from patients before and on treatment will be critical to the success of these efforts.
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12
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Gao L, Jiang W, Yue Q, Ye R, Li Y, Hong J, Zhang M. Radiomic model to predict the expression of PD-1 and overall survival of patients with ovarian cancer. Int Immunopharmacol 2022; 113:109335. [DOI: 10.1016/j.intimp.2022.109335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 09/29/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
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13
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Cardillo N, Devor EJ, Pedra Nobre S, Newtson A, Leslie K, Bender DP, Smith BJ, Goodheart MJ, Gonzalez-bosquet J. Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer. Cancers (Basel) 2022; 14:3554. [PMID: 35884615 PMCID: PMC9323510 DOI: 10.3390/cancers14143554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/12/2022] [Accepted: 07/18/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Approximately 30% of patients with advanced, high-grade serous ovarian cancer who undergo surgery will have a suboptimal result, resulting in decreased overall survival. Improving the ability to predict a successful surgery would improve survival. We aimed to use tumor genomics to create prediction models, which would predict an optimal or complete cytoreduction prior to entering the operating room. We created two sets of models, one for optimal and one for complete cytoreduction. We then validated those models using the TCGA database as well as statistical learning. We developed 21 models for optimal cytoreduction and 37 models for complete cytoreduction, which have the potential to improve our ability to predict these surgical results in patients with ovarian cancer before taking them to the operating room. Improving our pre-operative decision-making will result in more patients having the desired surgical results and, therefore, improved survival. Abstract Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.
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14
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Shrestha P, Poudyal B, Yadollahi S, E. Wright D, V. Gregory A, D. Warner J, Korfiatis P, C. Green I, L. Rassier S, Mariani A, Kim B, K. Laughlin-Tommaso S, L. Kline T. A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions. Gynecol Oncol 2022; 166:596-605. [PMID: 35914978 DOI: 10.1016/j.ygyno.2022.07.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from image reconstruction to image processing and automated analysis. In contrast to other areas, such as brain and breast imaging, the impacts of AI have not been as strongly felt in gynecologic imaging. In this review article, we: (i) provide a background of clinically relevant AI concepts, (ii) describe methods and approaches in computer vision, and (iii) highlight prior work related to image classification tasks utilizing AI approaches in gynecologic imaging. DATA SOURCES A comprehensive search of several databases from each database's inception to March 18th, 2021, English language, was conducted. The databases included Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, and Ovid Cochrane Database of Systematic Reviews and ClinicalTrials.gov. METHODS OF STUDY SELECTION We performed an extensive literature review with 61 articles curated by three reviewers and subsequent sorting by specialists using specific inclusion and exclusion criteria. TABULATION, INTEGRATION, AND RESULTS We summarize the literature grouped by each of the three most common gynecologic malignancies: endometrial, cervical, and ovarian. For each, a brief introduction encapsulating the AI methods, imaging modalities, and clinical parameters in the selected articles is presented. We conclude with a discussion of current developments, trends and limitations, and suggest directions for future study. CONCLUSION This review article should prove useful for collaborative teams performing research studies targeted at the incorporation of radiological imaging and AI methods into gynecological clinical practice.
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15
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Rundo L, Beer L, Escudero Sanchez L, Crispin-Ortuzar M, Reinius M, McCague C, Sahin H, Bura V, Pintican R, Zerunian M, Ursprung S, Allajbeu I, Addley H, Martin-Gonzalez P, Buddenkotte T, Singh N, Sahdev A, Funingana IG, Jimenez-Linan M, Markowetz F, Brenton JD, Sala E, Woitek R. Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma. Front Oncol 2022; 12:868265. [PMID: 35785153 PMCID: PMC9243357 DOI: 10.3389/fonc.2022.868265] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022] Open
Abstract
Background Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard. Methods Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response). Results The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models. Conclusions CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - Lucian Beer
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lorena Escudero Sanchez
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Marika Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Cathal McCague
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - Hilal Sahin
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey
| | - Vlad Bura
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - Roxana Pintican
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
- Department of Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Marta Zerunian
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome—Sant’Andrea University Hospital, Rome, Italy
| | | | - Iris Allajbeu
- Department of Radiology, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Helen Addley
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Thomas Buddenkotte
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Naveena Singh
- Department of Clinical Pathology, Barts Health NHS Trust, London, United Kingdom
| | - Anju Sahdev
- Department of Radiology, Barts Health NHS Trust, London, United Kingdom
| | - Ionut-Gabriel Funingana
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - James D. Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Ramona Woitek
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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16
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Avesani G, Tran HE, Cammarata G, Botta F, Raimondi S, Russo L, Persiani S, Bonatti M, Tagliaferri T, Dolciami M, Celli V, Boldrini L, Lenkowicz J, Pricolo P, Tomao F, Rizzo SMR, Colombo N, Manganaro L, Fagotti A, Scambia G, Gui B, Manfredi R. CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset. Cancers (Basel) 2022; 14:2739. [PMID: 35681720 DOI: 10.3390/cancers14112739] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/15/2022] [Accepted: 05/29/2022] [Indexed: 02/04/2023] Open
Abstract
PURPOSE Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. METHODS Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. RESULTS We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). CONCLUSIONS In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.
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17
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Li B, Sun M, Yao P, Chang Z, Liu Z. Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer. J Comput Assist Tomogr 2022; 46:371-8. [DOI: 10.1097/rct.0000000000001279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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18
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Qi L, Chen D, Li C, Li J, Wang J, Zhang C, Li X, Qiao G, Wu H, Zhang X, Ma W. Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors. Front Genet 2021; 12:753948. [PMID: 34650603 PMCID: PMC8505695 DOI: 10.3389/fgene.2021.753948] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives: To establish and validate a nomogram integrating radiomics signatures from ultrasound and clinical factors to discriminate between benign, borderline, and malignant serous ovarian tumors. Materials and methods: In this study, a total of 279 pathology-confirmed serous ovarian tumors collected from 265 patients between March 2013 and December 2016 were used. The training cohort was generated by randomly selecting 70% of each of the three types (benign, borderline, and malignant) of tumors, while the remaining 30% was included in the validation cohort. From the transabdominal ultrasound scanning of ovarian tumors, the radiomics features were extracted, and a score was calculated. The ability of radiomics to differentiate between the grades of ovarian tumors was tested by comparing benign vs borderline and malignant (task 1) and borderline vs malignant (task 2). These results were compared with the diagnostic performance and subjective assessment by junior and senior sonographers. Finally, a clinical-feature alone model and a combined clinical-radiomics (CCR) model were built using predictive nomograms for the two tasks. Receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA) were performed to evaluate the model performance. Results: The US-based radiomics models performed satisfactorily in both the tasks, showing especially higher accuracy in the second task by successfully discriminating borderline and malignant ovarian serous tumors compared to the evaluations by senior sonographers (AUC = 0.789 for seniors and 0.877 for radiomics models in task one; AUC = 0.612 for senior and 0.839 for radiomics model in task 2). We showed that the CCR model, comprising CA125 level, lesion location, ascites, and radiomics signatures, performed the best (AUC = 0.937, 95%CI 0.905-0.969 in task 1, AUC = 0.924, 95%CI 0.876-0.971 in task 2) in the training as well as in the validation cohorts (AUC = 0.914, 95%CI 0.851-0.976 in task 1, AUC = 0.890, 95%CI 0.794-0.987 in task 2). The calibration curve and DCA analysis of the CCR model more accurately predicted the classification of the tumors than the clinical features alone. Conclusion: This study integrates novel radiomics signatures from ultrasound and clinical factors to create a nomogram to provide preoperative diagnostic information for differentiating between benign, borderline, and malignant ovarian serous tumors, thereby reducing unnecessary and risky biopsies and surgeries.
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Affiliation(s)
- Lisha Qi
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Dandan Chen
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chunxiang Li
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Ultrasonographic Diagnosis and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jinghan Li
- Department of Ultrasonographic Diagnosis and Therapy, Tianjin Ninghe Hospital, Tianjin, China
| | - Jingyi Wang
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chao Zhang
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiaofeng Li
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ge Qiao
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Haixiao Wu
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiaofang Zhang
- Department of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin, China
| | - Wenjuan Ma
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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Oliveira LRLBD, Horvat N, Andrieu PIC, Panizza PSB, Cerri GG, Viana PCC. Ovarian cancer staging: What the surgeon needs to know. Br J Radiol 2021; 94:20210091. [PMID: 34289310 DOI: 10.1259/bjr.20210091] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Ovarian cancer (OC) is the leading cause of gynecological cancer death, and most cases are diagnosed at advanced stages due to a nonspecific and insidious clinical presentation. Radiologists play a critical role in the decision of which patients are candidates for primary debulking surgery and who may benefit from neoadjuvant chemotherapy. This pictorial review summarizes the dissemination patterns of OC, main imaging findings of metastatic disease, and which findings may alter the treatment plan or predict suboptimal tumor resection.
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Affiliation(s)
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, United States
| | - Pamela Ines Causa Andrieu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, United States
| | | | - Giovanni Guido Cerri
- Department of Radiology, Hospital Sirio-Libanes, São Paulo, Brazil.,Department of Radiology, University of Sao Paulo, São Paulo, Brazil
| | - Publio Cesar Cavalcante Viana
- Department of Radiology, Hospital Sirio-Libanes, São Paulo, Brazil.,Department of Radiology, University of Sao Paulo, São Paulo, Brazil.,Department of Interventional Radiology, University of Sao Paulo, Sao Paulo, Brazil.,Department of Interventional Radiology, Hospital Sirio-Libanes, Sao Paulo, Brazil
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20
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Crispin-Ortuzar M, Sala E. Precision radiogenomics: fusion biopsies to target tumour habitats in vivo. Br J Cancer 2021; 125:778-9. [PMID: 33864016 DOI: 10.1038/s41416-021-01381-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/17/2021] [Accepted: 03/24/2021] [Indexed: 11/16/2022] Open
Abstract
High-grade serous ovarian cancer lesions display a high degree of heterogeneity on CT scans. We have recently shown that regions with distinct imaging profiles can be accurately biopsied in vivo using a technique based on the fusion of CT and ultrasound scans.
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