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Russo L, Charles-Davies D, Bottazzi S, Sala E, Boldrini L. Radiomics for clinical decision support in radiation oncology. Clin Oncol (R Coll Radiol) 2024; 36:e269-e281. [PMID: 38548581 DOI: 10.1016/j.clon.2024.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/14/2024] [Accepted: 03/08/2024] [Indexed: 07/09/2024]
Abstract
Radiomics is a promising tool for the development of quantitative biomarkers to support clinical decision-making. It has been shown to improve the prediction of response to treatment and outcome in different settings, particularly in the field of radiation oncology by optimising the dose delivery solutions and reducing the rate of radiation-induced side effects, leading to a fully personalised approach. Despite the promising results offered by radiomics at each of these stages, standardised methodologies, reproducibility and interpretability of results are still lacking, limiting the potential clinical impact of these tools. In this review, we briefly describe the principles of radiomics and the most relevant applications of radiomics at each stage of cancer management in the framework of radiation oncology. Furthermore, the integration of radiomics into clinical decision support systems is analysed, defining the challenges and offering possible solutions for translating radiomics into a clinically applicable tool.
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Affiliation(s)
- L Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy.
| | - D Charles-Davies
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - S Bottazzi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - E Sala
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Dipartimento di Scienze Radiologiche ed Ematologiche. Università Cattolica Del Sacro Cuore, Rome, Italy
| | - L Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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2
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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] [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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Yang EJ, Lee AJ, Hwang WY, Chang SJ, Kim HS, Kim NK, Kim Y, Kong TW, Lee EJ, Park SJ, Son JH, Suh DH, Son DH, Shim SH. Lymphadenectomy in clinically early epithelial ovarian cancer and survival analysis (LILAC): a Gynecologic Oncology Research Investigators Collaboration (GORILLA-3002) retrospective study. J Gynecol Oncol 2024; 35:35.e75. [PMID: 38497109 DOI: 10.3802/jgo.2024.35.e75] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/13/2024] [Accepted: 02/25/2024] [Indexed: 03/19/2024] Open
Abstract
OBJECTIVE This study aimed to evaluate the therapeutic role of lymphadenectomy in patients surgically treated for clinically early-stage epithelial ovarian cancer (EOC). METHODS This retrospective, multicenter study included patients with clinically early-stage EOC based on preoperative abdominal-pelvic computed tomography or magnetic resonance imaging findings between 2007 and 2021. Oncologic outcomes and perioperative complications were compared between the lymphadenectomy and non-lymphadenectomy groups. Independent prognostic factors were determined using Cox regression analysis. Disease-free survival (DFS) was the primary outcome. Overall survival (OS) and perioperative outcomes were the secondary outcomes. RESULTS In total, 586 patients (lymphadenectomy group, n=453 [77.3%]; non-lymphadenectomy groups, n=133 [22.7%]) were eligible. After surgical staging, upstaging was identified based on the presence of lymph node metastasis in 14 (3.1%) of 453 patients. No significant difference was found in the 5-year DFS (88.9% vs. 83.4%, p=0.203) and 5-year OS (97.2% vs. 97.7%, p=0.895) between the two groups. Using multivariable analysis, lymphadenectomy was not significantly associated with DFS or OS. However, using subgroup analysis, the lymphadenectomy group with serous histology had higher 5-year DFS rates than did the non-lymphadenectomy group (86.5% vs. 74.4%, p=0.048; adjusted hazard ratio=0.281; 95% confidence interval=0.107-0.735; p=0.010). The lymphadenectomy group had longer operating time (p<0.001), higher estimated blood loss (p<0.001), and higher perioperative complication rate (p=0.004) than did the non-lymphadenectomy group. CONCLUSION In patients with clinically early-stage EOC with serous histology, lymphadenectomy was associated with survival benefits. Considering its potential harm, lymphadenectomy should be performed according to histologic subtype and subsequent chemotherapy in patients with clinically early-stage EOC. TRIAL REGISTRATION Clinical Research Information Service Identifier: KCT0007309.
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Affiliation(s)
- Eun Jung Yang
- Department of Obstetrics and Gynecology, Soonchunhyang University Cheonan Hospital, Cheonan, Korea
| | - A Jin Lee
- Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea
| | - Woo Yeon Hwang
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Suk-Joon Chang
- Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea
| | - Hee Seung Kim
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Nam Kyeong Kim
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Yeorae Kim
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Tae Wook Kong
- Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea
| | - Eun Ji Lee
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Soo Jin Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Joo-Hyuk Son
- Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea
| | - Dong Hoon Suh
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dong Hee Son
- Research Coordinating Center, Konkok University Medical Center, Seoul, Korea
| | - Seung-Hyuk Shim
- Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea.
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Demetriou D, Lockhat Z, Brzozowski L, Saini KS, Dlamini Z, Hull R. The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics. Cancers (Basel) 2024; 16:1076. [PMID: 38473432 DOI: 10.3390/cancers16051076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.
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Affiliation(s)
- Demetra Demetriou
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Zarina Lockhat
- Department of Radiology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Luke Brzozowski
- Translational Research and Core Facilities, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Kamal S Saini
- Fortrea Inc., 8 Moore Drive, Durham, NC 27709, USA
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Zodwa Dlamini
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Rodney Hull
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
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5
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Leng Y, Kan A, Wang X, Li X, Xiao X, Wang Y, Liu L, Gong L. Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study. BMC Cancer 2024; 24:307. [PMID: 38448945 PMCID: PMC10916071 DOI: 10.1186/s12885-024-12037-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 02/21/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. METHODS A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. RESULTS Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001). CONCLUSIONS The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.
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Affiliation(s)
- Yinping Leng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Ao Kan
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Xiwen Wang
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Xiaofen Li
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, Jiangxi, China
| | - Xuan Xiao
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China
| | - Yu Wang
- Clinical and Technical Support, Philips Healthcare, Shanghai, China
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, Jiangxi, China.
| | - Lianggeng Gong
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Minde Road No. 1, 330006, Nanchang, Jiangxi Province, China.
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Prior O, Macarro C, Navarro V, Monreal C, Ligero M, Garcia-Ruiz A, Serna G, Simonetti S, Braña I, Vieito M, Escobar M, Capdevila J, Byrne AT, Dienstmann R, Toledo R, Nuciforo P, Garralda E, Grussu F, Bernatowicz K, Perez-Lopez R. Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer. Radiol Artif Intell 2024; 6:e230118. [PMID: 38294307 PMCID: PMC10982821 DOI: 10.1148/ryai.230118] [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: 04/12/2023] [Revised: 10/18/2023] [Accepted: 01/07/2024] [Indexed: 02/01/2024]
Abstract
Purpose To identify precise three-dimensional radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL of ICC ≥ 0.50). Habitats were obtained with Gaussian mixture models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI, and tumor biopsies. Results Three-dimensional radiomics showed poor repeatability (LCL of ICC: median [IQR], 0.442 [0.312-0.516]) and poor reproducibility against kernel radius (LCL of ICC: median [IQR], 0.440 [0.33-0.526]) but excellent reproducibility against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]). Twenty-six radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC: median [IQR], 0.601 [0.494-0.712] and 0.651 [0.52-0.784] for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC: median [IQR], 0.532 [0.424-0.637] and 0.587 [0.465-0.703] for lung and liver lesions, respectively; P < .001). In the case study, CT habitats correlated quantitatively and qualitatively with heterogeneity observed in multiparametric MRI habitats and histology. Conclusion Precise three-dimensional radiomics features were identified on CT images that enabled tumor heterogeneity assessment through stable tumor habitat computation. Keywords: CT, Diffusion-weighted Imaging, Dynamic Contrast-enhanced MRI, MRI, Radiomics, Unsupervised Learning, Oncology, Liver, Lung Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Sagreiya in this issue.
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Affiliation(s)
- Olivia Prior
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Carlos Macarro
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Víctor Navarro
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Camilo Monreal
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Marta Ligero
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Alonso Garcia-Ruiz
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Garazi Serna
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Sara Simonetti
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Irene Braña
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Maria Vieito
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Manuel Escobar
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Jaume Capdevila
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Annette T. Byrne
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Rodrigo Dienstmann
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Rodrigo Toledo
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Paolo Nuciforo
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Elena Garralda
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
| | - Francesco Grussu
- From the Radiomics Group, Vall d’Hebron Institute of Oncology,
Carrer de Natzaret 115–117, Barcelona 08035, Spain (O.P., C. Macarro, C.
Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall
d’Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular
Pathology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
(G.S., S.S., P.N.); Department of Medical Oncology, Vall d’Hebron
University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular
Therapeutic Research Unit, Vall d’Hebron Institute of Oncology,
Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall
d’Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and
Clonal Dynamics Group, Vall d’Hebron Institute of Oncology (VHIO),
Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre
for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
(A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland
(A.T.B.)
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7
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Chen J, Liu L, He Z, Su D, Liu C. CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:180-195. [PMID: 38343232 DOI: 10.1007/s10278-023-00903-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/12/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.
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Affiliation(s)
- Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Lei Liu
- School of Computer Science and Engineering, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, Guangxi, People's Republic of China
| | - Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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8
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Miceli V, Gennarini M, Tomao F, Cupertino A, Lombardo D, Palaia I, Curti F, Riccardi S, Ninkova R, Maccioni F, Ricci P, Catalano C, Rizzo SMR, Manganaro L. Imaging of Peritoneal Carcinomatosis in Advanced Ovarian Cancer: CT, MRI, Radiomic Features and Resectability Criteria. Cancers (Basel) 2023; 15:5827. [PMID: 38136373 PMCID: PMC10741537 DOI: 10.3390/cancers15245827] [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: 10/27/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
PC represents the most striking picture of the loco-regional spread of ovarian cancer, configuring stage III. In the last few years, many papers have evaluated the role of imaging and therapeutic management in patients with ovarian cancer and PC. This paper summed up the literature on traditional approaches to the imaging of peritoneal carcinomatosis in advanced ovarian cancer, presenting classification systems, most frequent patterns, routes of spread and sites that are difficult to identify. The role of imaging in diagnosis was investigated, with particular attention to the reported sensitivity and specificity data-computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography-CT (PET-CT)-and to the peritoneal cancer index (PCI). In addition, we explored the therapeutic possibilities and radiomics applications that can impact management of patients with ovarian cancer. Careful staging is mandatory, and patient selection is one of the most important factors influencing complete cytoreduction (CCR) outcome: an accurate pre-operative imaging may allow selection of patients that may benefit most from primary cytoreductive surgery.
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Affiliation(s)
- Valentina Miceli
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Marco Gennarini
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Federica Tomao
- Department of Gynecological, Obstetrical and Urological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (F.T.); (I.P.)
| | - Angelica Cupertino
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Dario Lombardo
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Innocenza Palaia
- Department of Gynecological, Obstetrical and Urological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (F.T.); (I.P.)
| | - Federica Curti
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Sandrine Riccardi
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Roberta Ninkova
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Francesca Maccioni
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Paolo Ricci
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Carlo Catalano
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
| | - Stefania Maria Rita Rizzo
- Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), 6900 Lugano, Switzerland;
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana (USI), 6900 Lugano, Switzerland
| | - Lucia Manganaro
- Department of Radiological, Oncology and Patological Sciences, “Sapienza” University of Rome, 00185 Rome, Italy; (V.M.); (M.G.); (A.C.); (D.L.); (F.C.); (S.R.); (R.N.); (F.M.); (P.R.); (C.C.)
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9
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Sadowski EA, Lees B, McMillian AB, Kusmirek JE, Cho SY, Barroilhet LM. Distribution of prostate specific membrane antigen (PSMA) on PET-MRI in patients with and without ovarian cancer. Abdom Radiol (NY) 2023; 48:3643-3652. [PMID: 37261441 DOI: 10.1007/s00261-023-03957-3] [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: 02/13/2023] [Revised: 05/09/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023]
Abstract
OBJECTIVES Ovarian cancer is the most lethal cancer and future research needs to focus on the early detection and exploration of new therapeutic agents. The objectives of this proof-of-concept study are to assess the feasibility of PSMA 18F-DCFPyl PET/MR imaging for detecting ovarian cancer and to evaluate the PSMA distribution in patients with and without ovarian cancer. METHODS This prospective pilot proof-of-concept study in patients with and without ovarian cancers occurred between October 2017 and January 2020. Patients were recruited from gynecologic oncology or hereditary ovarian cancer clinics, and underwent surgical removal of the uterus and ovaries for gynecologic indications. PSMA 18F-DCFPyl PET/MRI was obtained prior to standard of care surgery. RESULTS Fourteen patients were scanned: four patients with normal ovaries, six patients with benign ovarian lesions, and four patients with malignant ovarian lesions. Tracer uptake in normal ovaries (SUVmax = 2.8 ± 0.4) was greater than blood pool (SUVmax = 1.8 ± 0.5, p < 0.0001). Tracer uptake in benign ovarian lesions (2.2 ± 1.0) did not differ significantly from blood pool (p = 0.331). Tracer uptake in ovarian cancer (SUVmax = 7.8 ± 3.8) was greater than blood pool (p < 0.0001), normal ovaries (p = 0.0014), and benign ovarian lesions (p = 0.005). CONCLUSION PET/MR imaging detected PSMA uptake in ovarian cancer, with little to no uptake in benign ovarian findings. These results are encouraging and further studies in a larger patient cohort would be useful to help determine the extent and heterogeneity of PSMA uptake in ovarian cancer patients.
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Affiliation(s)
- Elizabeth A Sadowski
- Departments of Radiology, Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372, Madison, WI, 53792-3252, USA.
| | - Brittany Lees
- Atrium Health Levine Cancer Institute, 1021 Morehead Medical Drive, Suite 2100, Charlotte, NC, 28204, USA
| | - Alan B McMillian
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 1111 Highland Avenue, Rm 1139, Madison, WI, 53705, USA
| | - Joanna E Kusmirek
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., E3/372, Madison, WI, 53792-3252, USA
| | - Steve Y Cho
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., E3/372, Madison, WI, 53792-3252, USA
| | - Lisa M Barroilhet
- Departments of Radiology, Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372, Madison, WI, 53792-3252, USA
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10
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Travaglio Morales D, Huerga Cabrerizo C, Losantos García I, Coronado Poggio M, Cordero García JM, Llobet EL, Monachello Araujo D, Rizkallal Monzón S, Domínguez Gadea L. Prognostic 18F-FDG Radiomic Features in Advanced High-Grade Serous Ovarian Cancer. Diagnostics (Basel) 2023; 13:3394. [PMID: 37998530 PMCID: PMC10670627 DOI: 10.3390/diagnostics13223394] [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: 09/27/2023] [Revised: 11/03/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023] Open
Abstract
High-grade serous ovarian cancer (HGSOC) is an aggressive disease with different clinical outcomes and poor prognosis. This could be due to tumor heterogeneity. The 18F-FDG PET radiomic parameters permit addressing tumor heterogeneity. Nevertheless, this has been not well studied in ovarian cancer. The aim of our work was to assess the prognostic value of pretreatment 18F-FDG PET radiomic features in patients with HGSOC. A review of 36 patients diagnosed with advanced HGSOC between 2016 and 2020 in our center was performed. Radiomic features were obtained from pretreatment 18F-FDGPET. Disease-free survival (DFS) and overall survival (OS) were calculated. Optimal cutoff values with receiver operating characteristic curve/median values were used. A correlation between radiomic features and DFS/OS was made. The mean DFS was 19.6 months and OS was 37.1 months. Total Lesion Glycolysis (TLG), GLSZM_ Zone Size Non-Uniformity (GLSZM_ZSNU), and GLRLM_Run Length Non-Uniformity (GLRLM_RLNU) were significantly associated with DFS. The survival-curves analysis showed a significant difference of DSF in patients with GLRLM_RLNU > 7388.3 versus patients with lower values (19.7 months vs. 31.7 months, p = 0.035), maintaining signification in the multivariate analysis (p = 0.048). Moreover, Intensity-based Kurtosis was associated with OS (p = 0.027). Pretreatment 18F-FDG PET radiomic features GLRLM_RLNU, GLSZM_ZSNU, and Kurtosis may have prognostic value in patients with advanced HGSOC.
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Affiliation(s)
- Daniela Travaglio Morales
- Nuclear Medicine Department, La Paz University Hospital, 28046 Madrid, Spain
- Nuclear Medicine Department, Halle University Hospital, 06120 Halle, Germany
| | - Carlos Huerga Cabrerizo
- Department of Medical Physics and Radiation Protection, La Paz University Hospital, 28046 Madrid, Spain
| | | | | | | | - Elena López Llobet
- Nuclear Medicine Department, La Paz University Hospital, 28046 Madrid, Spain
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Herzog TJ, Wahab SA, Mirza MR, Pothuri B, Vergote I, Graybill WS, Malinowska IA, York W, Hurteau JA, Gupta D, González-Martin A, Monk BJ. Optimizing disease progression assessment using blinded central independent review and comparing it with investigator assessment in the PRIMA/ENGOT-ov26/GOG-3012 trial: challenges and solutions. Int J Gynecol Cancer 2023; 33:1733-1742. [PMID: 37931976 PMCID: PMC10646892 DOI: 10.1136/ijgc-2023-004605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/26/2023] [Indexed: 11/08/2023] Open
Abstract
OBJECTIVE Progression-free survival is an established clinically meaningful endpoint in ovarian cancer trials, but it may be susceptible to bias; therefore, blinded independent centralized radiological review is often included in trial designs. We compared blinded independent centralized review and investigator-assessed progressive disease performance in the PRIMA/ENGOT-ov26/GOG-3012 trial examining niraparib monotherapy. METHODS PRIMA/ENGOT-ov26/GOG-3012 was a randomized, double-blind phase 3 trial; patients with newly diagnosed stage III/IV ovarian cancer received niraparib or placebo. The primary endpoint was progression-free survival (per Response Evaluation Criteria in Solid Tumors [RECIST] v1.1), determined by two independent radiologists, an arbiter if required, and by blinded central clinician review. Discordance rates between blinded independent centralized review and investigator assessment of progressive disease and non-progressive disease were routinely assessed. To optimize disease assessment, a training intervention was developed for blinded independent centralized radiological reviewers, and RECIST refresher training was provided for investigators. Discordance rates were determined post-intervention. RESULTS There was a 39% discordance rate between blinded independent centralized review and investigator-assessed progressive disease/non-progressive disease in an initial patient subset (n=80); peritoneal carcinomatosis was the most common source of discordance. All reviewers underwent training, and as a result, changes were implemented, including removal of two original reviewers and identification of 10 best practices for reading imaging data. Post-hoc analysis indicated final discordance rates between blinded independent centralized review and investigator improved to 12% in the overall population. Median progression-free survival and hazard ratios were similar between blinded independent centralized review and investigators in the overall population and across subgroups. CONCLUSION PRIMA/ENGOT-ov26/GOG-3012 highlights the need to optimize blinded independent centralized review and investigator concordance using early, specialized, ovarian-cancer-specific radiology training to maximize validity of outcome data.
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Affiliation(s)
- Thomas J Herzog
- Department of Obstetrics and Gynecology, University of Cincinnati Cancer Center, Cincinnati, Ohio, USA
| | - Shaun A Wahab
- Department of Radiology, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
| | - Mansoor R Mirza
- Department of Oncology, Nordic Society of Gynaecological Oncology Clinical Trial Unit (NSGO-CTU) and Rigshospitalet-Copenhagen University Hospital, Copenhagen, Denmark
| | - Bhavana Pothuri
- Department of Obstetrics and Gynecology, NYU Langone Health Perlmutter Cancer Center, New York, New York, USA
| | - Ignace Vergote
- Department of Obstetrics and Gynecology, Leuven Cancer Institute, Catholic University Leuven, Leuven, Belgium
| | - Whitney S Graybill
- Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina, USA
| | | | - Whitney York
- Oncology Statistics, GSK, Upper Providence, Pennsylvania, USA
| | - Jean A Hurteau
- Synthetic Lethality & Immuno-oncology, GSK, Waltham, Massachusetts, USA
| | - Divya Gupta
- Synthetic Lethality, GSK, Waltham, Massachusetts, USA
| | - Antonio González-Martin
- Department of Medical Oncology, Grupo Español de Investigación en Cáncer de Ovario (GEICO), Program in Solid Tumors, Center for Applied Medical Research (CIMA), Madrid, Spain
| | - Bradley J Monk
- Department of Obstetrics and Gynecology, HonorHealth Research Institute, University of Arizona College of Medicine, Phoenix, Arizona, USA
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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] [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] [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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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] [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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Jan YT, Tsai PS, Huang WH, Chou LY, Huang SC, Wang JZ, Lu PH, Lin DC, Yen CS, Teng JP, Mok GSP, Shih CT, Wu TH. Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors. Insights Imaging 2023; 14:68. [PMID: 37093321 PMCID: PMC10126170 DOI: 10.1186/s13244-023-01412-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. METHODS We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. RESULTS Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. CONCLUSIONS We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.
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Affiliation(s)
- Ya-Ting Jan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Pei-Shan Tsai
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Wen-Hui Huang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Ling-Ying Chou
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Shih-Chieh Huang
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Jing-Zhe Wang
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Pei-Hsuan Lu
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Dao-Chen Lin
- Division of Endocrine and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Sheng Yen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Ju-Ping Teng
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Cheng-Ting Shih
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, 404, Taiwan.
| | - Tung-Hsin Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.
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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] [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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Delgado-Ortet M, Reinius MAV, McCague C, Bura V, Woitek R, Rundo L, Gill AB, Gehrung M, Ursprung S, Bolton H, Haldar K, Pathiraja P, Brenton JD, Crispin-Ortuzar M, Jimenez-Linan M, Escudero Sanchez L, Sala E. Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Front Oncol 2023; 13:1085874. [PMID: 36860310 PMCID: PMC9969130 DOI: 10.3389/fonc.2023.1085874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
Background High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours. Methods In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process. Results Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments. Conclusions We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.
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Affiliation(s)
- Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Marika A. V. Reinius
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Vlad Bura
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Radiology, Clinical Emergency Children’s Hospital, Cluj-Napoca, Romania
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Research Center for Medical Image Analysis & Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy
| | - Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Marcel Gehrung
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Helen Bolton
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Krishnayan Haldar
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Pubudu Pathiraja
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - James D. Brenton
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
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Xiao X, Wang Z, Kong Y, Lu H. Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images. Front Oncol 2023; 13:1081529. [PMID: 36845699 PMCID: PMC9945212 DOI: 10.3389/fonc.2023.1081529] [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: 11/01/2022] [Accepted: 01/20/2023] [Indexed: 02/11/2023] Open
Abstract
Colorectal cancer (CRC) is now the third most common malignancy to cause mortality worldwide, and its prognosis is of great importance. Recent CRC prognostic prediction studies mainly focused on biomarkers, radiometric images, and end-to-end deep learning methods, while only a few works paid attention to exploring the relationship between the quantitative morphological features of patients' tissue slides and their prognosis. However, existing few works in this area suffered from the drawback of choosing the cells randomly from the whole slides, which contain the non-tumor region that lakes information about prognosis. In addition, the existing works, which tried to demonstrate their biological interpretability using patients' transcriptome data, failed to show the biological meaning closely related to cancer. In this study, we proposed and evaluated a prognostic model using morphological features of cells in the tumor region. The features were first extracted by the software CellProfiler from the tumor region selected by Eff-Unet deep learning model. Features from different regions were then averaged for each patient as their representative, and the Lasso-Cox model was used to select the prognosis-related features. The prognostic prediction model was at last constructed using the selected prognosis-related features and was evaluated through KM estimate and cross-validation. In terms of biological meaning, Gene Ontology (GO) enrichment analysis of the expressed genes that correlated with the prognostically significant features was performed to show the biological interpretability of our model.With the help of tumor segmentation, our model achieved better statistical significance and better biological interpretability compared to the results without tumor segmentation. Statistically, the Kaplan Meier (KM) estimate of our model showed that the model using features in the tumor region has a higher C-index, a lower p-value, and a better performance on cross-validation than the model without tumor segmentation. In addition, revealing the pathway of the immune escape and the spread of the tumor, the model with tumor segmentation demonstrated a biological meaning much more related to cancer immunobiology than the model without tumor segmentation. Our prognostic prediction model using quantitive morphological features from tumor regions was almost as good as the TNM tumor staging system as they had a close C-index, and our model can be combined with the TNM tumor stage system to make a better prognostic prediction. And to the best of our knowledge, the biological mechanisms in our study were the most relevant to the immune mechanism of cancer compared to the previous studies.
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Affiliation(s)
- Xiao Xiao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China,Shanghai Jiao Tong University (SJTU)-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zuoheng Wang
- Department of Biostatistics, Yale University, New Haven, CT, United States
| | - Yan Kong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China,Shanghai Jiao Tong University (SJTU)-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Hui Lu, ; Yan Kong,
| | - Hui Lu
- Shanghai Jiao Tong University (SJTU)-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China,Center for Biomedical Informatics, Shanghai Children’s Hospital, Shanghai, China,*Correspondence: Hui Lu, ; Yan Kong,
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Binas DA, Tzanakakis P, Economopoulos TL, Konidari M, Bourgioti C, Moulopoulos LA, Matsopoulos GK. A Novel Approach for Estimating Ovarian Cancer Tissue Heterogeneity through the Application of Image Processing Techniques and Artificial Intelligence. Cancers (Basel) 2023; 15:cancers15041058. [PMID: 36831401 PMCID: PMC9954367 DOI: 10.3390/cancers15041058] [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: 01/13/2023] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
PURPOSE Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity. METHODS Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10-3 mm2/s). RESULTS The average recorded accuracy of the proposed automated classification system was equal to 0.86. CONCLUSION The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.
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Affiliation(s)
- Dimitrios A. Binas
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
- Correspondence:
| | - Petros Tzanakakis
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Theodore L. Economopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Marianna Konidari
- Department of Radiology, School of Medicine National and Kapodistrian University of Athens, Aretaieion Hospital, 11528 Athens, Greece
| | - Charis Bourgioti
- Department of Radiology, School of Medicine National and Kapodistrian University of Athens, Aretaieion Hospital, 11528 Athens, Greece
| | - Lia Angela Moulopoulos
- Department of Radiology, School of Medicine National and Kapodistrian University of Athens, Aretaieion Hospital, 11528 Athens, Greece
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
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20
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Cheng M, Tan S, Ren T, Zhu Z, Wang K, Zhang L, Meng L, Yang X, Pan T, Yang Z, Zhao X. Magnetic resonance imaging radiomics to differentiate ovarian sex cord-stromal tumors and primary epithelial ovarian cancers. Front Oncol 2023; 12:1073983. [PMID: 36713500 PMCID: PMC9880468 DOI: 10.3389/fonc.2022.1073983] [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: 10/19/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Objective To evaluate the diagnostic ability of magnetic resonance imaging (MRI) based radiomics and traditional characteristics to differentiate between Ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs). Methods We consecutively included a total of 148 patients with 173 tumors (81 SCSTs in 73 patients and 92 EOCs in 75 patients), who were randomly divided into development and testing cohorts at a ratio of 8:2. Radiomics features were extracted from each tumor, 5-fold cross-validation was conducted for the selection of stable features based on development cohort, and we built radiomics model based on these selected features. Univariate and multivariate analyses were used to identify the independent predictors in clinical features and conventional MR parameters for differentiating SCSTs and EOCs. And nomogram was used to visualized the ultimately predictive models. All models were constructed based on the logistic regression (LR) classifier. The performance of each model was evaluated by the receiver operating characteristic (ROC) curve. Calibration and decision curves analysis (DCA) were used to evaluate the performance of models. Results The final radiomics model was constructed by nine radiomics features, which exhibited superior predictive ability with AUCs of 0.915 (95%CI: 0.869-0.962) and 0.867 (95%CI: 0.732-1.000) in the development and testing cohorts, respectively. The mixed model which combining the radiomics signatures and traditional parameters achieved the best performance, with AUCs of 0.934 (95%CI: 0.892-0.976) and 0.875 (95%CI: 0.743-1.000) in the development and testing cohorts, respectively. Conclusion We believe that the radiomics approach could be a more objective and accurate way to distinguish between SCSTs and EOCs, and the mixed model developed in our study could provide a comprehensive, effective method for clinicians to develop an appropriate management strategy.
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Affiliation(s)
- Meiying Cheng
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Shifang Tan
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Tian Ren
- Department of Information, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zitao Zhu
- Medical College, Wuhan University, Wuhan, China
| | - Kaiyu Wang
- Magnetic resonance imaging (MRI) Research, GE Healthcare (China), Beijing, China
| | - Lingjie Zhang
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lingsong Meng
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xuhong Yang
- Department of Research, Huiying Medical Technology Co., Ltd., Beijing, China
| | - Teng Pan
- Department of Research, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Beijing, China
| | - Zhexuan Yang
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xin Zhao
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Xin Zhao,
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21
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Wan S, Zhou T, Che R, Li Y, Peng J, Wu Y, Gu S, Cheng J, Hua X. CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer. J Ovarian Res 2023; 16:1. [PMID: 36597144 PMCID: PMC9809527 DOI: 10.1186/s13048-022-01089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 12/23/2022] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE We aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. METHODS A total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction. RESULTS CCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model. CONCLUSION The expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.
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Affiliation(s)
- Sheng Wan
- grid.24516.340000000123704535Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China
| | - Tianfan Zhou
- grid.24516.340000000123704535Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China
| | - Ronghua Che
- grid.24516.340000000123704535Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China
| | - Ying Li
- grid.412793.a0000 0004 1799 5032Reproductive Medicine Center, Tongji Hospital Affiliated to Tongji University, Shanghai, China
| | - Jing Peng
- grid.24516.340000000123704535Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China
| | - Yuelin Wu
- grid.24516.340000000123704535Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China
| | - Shengyi Gu
- grid.24516.340000000123704535Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China
| | - Jiejun Cheng
- grid.24516.340000000123704535Department of Radiology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China ,grid.24516.340000000123704535Department of Radiology, Shanghai First Maternity and infant hospital, Shanghai Tongji University School of Medicine, 2699 West Gaoke Road, Shanghai, 201204 China
| | - Xiaolin Hua
- grid.24516.340000000123704535Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China ,grid.24516.340000000123704535Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092 China ,grid.24516.340000000123704535Department of Obstetrics, Shanghai First Maternity and infant hospital, Shanghai Tongji University School of Medicine, 2699 West Gaoke Road, Shanghai, 201204 China
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22
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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] [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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23
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What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies. Int J Mol Sci 2022; 23:ijms23126504. [PMID: 35742947 PMCID: PMC9224495 DOI: 10.3390/ijms23126504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.
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24
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Boehm KM, Aherne EA, Ellenson L, Nikolovski I, Alghamdi M, Vázquez-García I, Zamarin D, Long Roche K, Liu Y, Patel D, Aukerman A, Pasha A, Rose D, Selenica P, Causa Andrieu PI, Fong C, Capanu M, Reis-Filho JS, Vanguri R, Veeraraghavan H, Gangai N, Sosa R, Leung S, McPherson A, Gao J, Lakhman Y, Shah SP. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. NATURE CANCER 2022; 3:723-733. [PMID: 35764743 PMCID: PMC9239907 DOI: 10.1038/s43018-022-00388-9] [Citation(s) in RCA: 73] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 04/27/2022] [Indexed: 04/25/2023]
Abstract
Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.
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Affiliation(s)
- Kevin M Boehm
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY, USA
| | - Emily A Aherne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lora Ellenson
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ines Nikolovski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohammed Alghamdi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kara Long Roche
- Department of Surgical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ying Liu
- Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Druv Patel
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Aukerman
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arfath Pasha
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Doori Rose
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pier Selenica
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Chris Fong
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marinela Capanu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jorge S Reis-Filho
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Vanguri
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ramon Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samantha Leung
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - JianJiong Gao
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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25
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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:cancers14112739. [PMID: 35681720 PMCID: PMC9179845 DOI: 10.3390/cancers14112739] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [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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26
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Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer. J Comput Assist Tomogr 2022; 46:371-378. [DOI: 10.1097/rct.0000000000001279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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27
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Marofi F, Achmad H, Bokov D, Abdelbasset WK, Alsadoon Z, Chupradit S, Suksatan W, Shariatzadeh S, Hasanpoor Z, Yazdanifar M, Shomali N, Khiavi FM. Hurdles to breakthrough in CAR T cell therapy of solid tumors. Stem Cell Res Ther 2022; 13:140. [PMID: 35365241 PMCID: PMC8974159 DOI: 10.1186/s13287-022-02819-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/13/2022] [Indexed: 12/27/2022] Open
Abstract
Autologous T cells genetically engineered to express chimeric antigen receptor (CAR) have shown promising outcomes and emerged as a new curative option for hematological malignancy, especially malignant neoplasm of B cells. Notably, when T cells are transduced with CAR constructs, composed of the antigen recognition domain of monoclonal antibodies, they retain their cytotoxic properties in a major histocompatibility complex (MHC)-independent manner. Despite its beneficial effect, the current CAR T cell therapy approach faces myriad challenges in solid tumors, including immunosuppressive tumor microenvironment (TME), tumor antigen heterogeneity, stromal impediment, and tumor accessibility, as well as tribulations such as on-target/off-tumor toxicity and cytokine release syndrome (CRS). Herein, we highlight the complications that hamper the effectiveness of CAR T cells in solid tumors and the strategies that have been recommended to overcome these hurdles and improve infused T cell performance.
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Affiliation(s)
- Faroogh Marofi
- Immunology Research Center (IRC), Tabriz University of Medical Sciences, Tabriz, Iran
| | - Harun Achmad
- Department of Pediatric Dentistry, Faculty of Dentistry, Hasanuddin University, Makassar, Indonesia
| | - Dmitry Bokov
- Institute of Pharmacy, Sechenov First Moscow State Medical University, 8 Trubetskaya St., bldg. 2, Moscow, 119991, Russian Federation.,Laboratory of Food Chemistry, Federal Research Center of Nutrition, Biotechnology and Food Safety, 2/14 Ustyinsky pr., Moscow, 109240, Russian Federation
| | - Walid Kamal Abdelbasset
- Department of Health and Rehabilitation Sciences, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia.,Department of Physical Therapy, Kasr Al-Aini Hospital, Cairo University, Giza, Egypt
| | - Zeid Alsadoon
- Dentistry Department, College of Technical Engineering, The Islamic University, Najaf, Iraq
| | - Supat Chupradit
- Department of Occupational Therapy, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Wanich Suksatan
- Faculty of Nursing, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, 10210, Thailand
| | - Siavash Shariatzadeh
- Department of Pharmacology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Hasanpoor
- Department of Immunology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mahboubeh Yazdanifar
- Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Navid Shomali
- Immunology Research Center (IRC), Tabriz University of Medical Sciences, Tabriz, Iran
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Development of a radiomic-clinical nomogram for prediction of survival in patients with serous ovarian cancer. Clin Radiol 2022; 77:352-359. [PMID: 35264303 DOI: 10.1016/j.crad.2022.01.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 01/11/2022] [Indexed: 12/28/2022]
Abstract
AIM To develop and validate a radiomic-clinical nomogram to evaluate overall survival (OS) postoperatively in patients with serous ovarian cancer. MATERIALS AND METHODS Eighty serous ovarian cancer patients from The Cancer Imaging Archive (TCIA) database were used as the training set, and 39 eligible patients treated at Affiliated Huadu Hospital were used as the independent validation set. In total, 1,301 radiomics features were extracted from ovarian cancer lesions on venous-phase computed tomography (CT) images. Then, a radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm in the training set. Moreover, a radiomic-clinical nomogram was constructed incorporating the radiomics signature and clinical predictors based on a multivariable Cox regression analysis. The performance of the nomogram was evaluated. RESULTS Consisting of three selected features, the radiomics signature showed good discrimination in the training and validation sets with C-indexes of 0.694 (95% confidence interval [CI]: 0.613-0.775) and 0.709 (95% CI: 0.517-0.901), respectively. The radiomic-clinical nomogram contained the radiomics signature and four clinical predictors, including age, tumour size, pathological staging, and tumour grade. The nomogram showed favourable discrimination in the training set (C-index [95% CI], 0.754 [0.678-0.830]), which was confirmed in the validation set (C-index [95% CI], 0.727 [0.569-0.885]). According to the model, all patients were classified into high-risk and low-risk groups. Kaplan-Meier curves showed that there was a significant distinction between the OS of the high-risk and low-risk patients. CONCLUSIONS The proposed radiomic-clinical nomogram can increase the predictive accuracy of OS in patients with serous ovarian cancer after surgery, which may aid in clinical decision-making.
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Tardieu M, Lakhman Y, Khellaf L, Cardoso M, Sgarbura O, Colombo PE, Crispin-Ortuzar M, Sala E, Goze-Bac C, Nougaret S. Assessing Histology Structures by Ex Vivo MR Microscopy and Exploring the Link Between MRM-Derived Radiomic Features and Histopathology in Ovarian Cancer. Front Oncol 2022; 11:771848. [PMID: 35127479 PMCID: PMC8807492 DOI: 10.3389/fonc.2021.771848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/02/2021] [Indexed: 11/14/2022] Open
Abstract
The value of MR radiomic features at a microscopic scale has not been explored in ovarian cancer. The objective of this study was to probe the associations of MR microscopy (MRM) images and MRM-derived radiomic maps with histopathology in high-grade serous ovarian cancer (HGSOC). Nine peritoneal implants from 9 patients with HGSOC were imaged ex vivo with MRM using a 9.4-T MR scanner. All MRM images and computed pixel-wise radiomics maps were correlated with the slice-matched stroma and tumor proportion maps derived from whole histopathologic slide images (WHSI) of corresponding peritoneal implants. Automated MRM-derived segmentation maps of tumor and stroma were constructed using holdout test data and validated against the histopathologic gold standard. Excellent correlation between MRM images and WHSI was observed (Dice index = 0.77). Entropy, correlation, difference entropy, and sum entropy radiomic features were positively associated with high stromal proportion (r = 0.97,0.88, 0.81, and 0.96 respectively, p < 0.05). MR signal intensity, energy, homogeneity, auto correlation, difference variance, and sum average were negatively associated with low stromal proportion (r = –0.91, –0.93, –0.94, –0.9, –0.89, –0.89, respectively, p < 0.05). Using the automated model, MRM predicted stromal proportion with an accuracy ranging from 61.4% to 71.9%. In this hypothesis-generating study, we showed that it is feasible to resolve histologic structures in HGSOC using ex vivo MRM at 9.4 T and radiomics.
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Affiliation(s)
- Marion Tardieu
- Montpellier Cancer Research Institute (IRCM), INSERM U1194, University of Montpellier, Montpellier, France
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Lakhdar Khellaf
- Department of Pathology, Montpellier Cancer Institute (ICM), Montpellier, France
| | - Maida Cardoso
- BNIF Facility, L2C, UMR 5221, CNRS, University of Montpellier, Montpellier, France
| | - Olivia Sgarbura
- Montpellier Cancer Research Institute (IRCM), INSERM U1194, University of Montpellier, Montpellier, France
- Department of Surgery, Montpellier Cancer Institute (ICM), Montpellier, France
| | | | - Mireia Crispin-Ortuzar
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Evis Sala
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Christophe Goze-Bac
- BNIF Facility, L2C, UMR 5221, CNRS, University of Montpellier, Montpellier, France
| | - Stephanie Nougaret
- Montpellier Cancer Research Institute (IRCM), INSERM U1194, University of Montpellier, Montpellier, France
- Department of Radiology, Montpellier Cancer Institute (ICM), Montpellier, France
- *Correspondence: Stephanie Nougaret,
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30
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Fotopoulou C, Rockall A, Lu H, Lee P, Avesani G, Russo L, Petta F, Ataseven B, Waltering KU, Koch JA, Crum WR, Cunnea P, Heitz F, Harter P, Aboagye EO, du Bois A, Prader S. Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC). Br J Cancer 2021; 126:1047-1054. [PMID: 34923575 PMCID: PMC8979975 DOI: 10.1038/s41416-021-01662-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/23/2021] [Accepted: 12/01/2021] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality. METHODS RPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models. RESULTS The distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06-2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56-2.62; P = 0.00647). CONCLUSIONS RPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.
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Affiliation(s)
- Christina Fotopoulou
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.
| | - Andrea Rockall
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.,Department of Radiology, Imperial College Healthcare NHS Trust, London, W12 0HS, UK.,Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Haonan Lu
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Philippa Lee
- Department of Radiology, Imperial College Healthcare NHS Trust, London, W12 0HS, UK
| | - Giacomo Avesani
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.,Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.,Department of Imaging, Oncological Radiotherapy, and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Russo
- Department of Imaging, Oncological Radiotherapy, and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Federica Petta
- Department of Imaging, Oncological Radiotherapy, and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Beyhan Ataseven
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte, Henricistr.92, 45136, Essen, Germany.,Department of Obstetrics and Gynecology, University Hospital, LMU Munich, München, Germany
| | - Kai-Uwe Waltering
- Department of Radiology, Kliniken Essen-Mitte, Henricistr.92, 45136, Essen, Germany
| | - Jens Albrecht Koch
- Department of Radiology, Kliniken Essen-Mitte, Henricistr.92, 45136, Essen, Germany
| | - William R Crum
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.,Institute of Translational Medicine and Therapeutics (ITMAT), Imperial College, London, UK
| | - Paula Cunnea
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Florian Heitz
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte, Henricistr.92, 45136, Essen, Germany.,Department for Gynecology with the Center for Oncologic Surgery Charité Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Philipp Harter
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte, Henricistr.92, 45136, Essen, Germany
| | - Eric O Aboagye
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Andreas du Bois
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte, Henricistr.92, 45136, Essen, Germany
| | - Sonia Prader
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte, Henricistr.92, 45136, Essen, Germany.,Department of Obstetrics and Gynecology, Brixen General Hospital, Brixen, Italy.,Department of Obstetrics and Gynecology, Innsbruck Medical University, Innsbruck, Austria
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Wang H, Zhou Y, Wang X, Zhang Y, Ma C, Liu B, Kong Q, Yue N, Xu Z, Nie K. Reproducibility and Repeatability of CBCT-Derived Radiomics Features. Front Oncol 2021; 11:773512. [PMID: 34869015 PMCID: PMC8637922 DOI: 10.3389/fonc.2021.773512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/27/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. Methods The first-, second-, and fifth-day CBCT images from 10 head and neck (H&N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R2 as correlation parameters. Results The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs ≥ 0.80), while the kurtosis measurements were consistent in H&N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs ≥ 0.80) between packages in both H&N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test–retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&N and pelvic cohorts, showed R2 ≥0.8, meaning a high correlation with volume. Conclusion The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.
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Affiliation(s)
- Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.,Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.,Institute of Modern Physics, Fudan University, Shanghai, China
| | - Yongkang Zhou
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Chi Ma
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Bo Liu
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Ning Yue
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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Bernatowicz K, Grussu F, Ligero M, Garcia A, Delgado E, Perez-Lopez R. Robust imaging habitat computation using voxel-wise radiomics features. Sci Rep 2021; 11:20133. [PMID: 34635786 PMCID: PMC8505612 DOI: 10.1038/s41598-021-99701-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 09/27/2021] [Indexed: 12/19/2022] Open
Abstract
Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test–retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test–retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine.
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Affiliation(s)
- Kinga Bernatowicz
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035, Barcelona, Spain.
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035, Barcelona, Spain
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035, Barcelona, Spain
| | - Alonso Garcia
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035, Barcelona, Spain
| | - Eric Delgado
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035, Barcelona, Spain.,Radiology Department, Vall d'Hebron University Hospital, 08035, Barcelona, Spain
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Engbersen MP, Van Driel W, Lambregts D, Lahaye M. The role of CT, PET-CT, and MRI in ovarian cancer. Br J Radiol 2021; 94:20210117. [PMID: 34415198 DOI: 10.1259/bjr.20210117] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
New treatment developments in ovarian cancer have led to a renewed interest in staging advanced ovarian cancer. The treatment of females with ovarian cancer patients has a strong multidisciplinary character with an essential role for the radiologist. This review aims to provide an overview of the current position of CT, positron emission tomography-CT, and MRI in ovarian cancer and how imaging can be used to guide multidisciplinary team discussions.
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Affiliation(s)
- Maurits Peter Engbersen
- Department of Radiology, Antoni van Leeuwenhoek-Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Willemien Van Driel
- Department of Gynecology, Center of Gynecological Oncology Amsterdam, Antoni van Leeuwenhoek- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Doenja Lambregts
- Department of Radiology, Antoni van Leeuwenhoek-Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Max Lahaye
- Department of Radiology, Antoni van Leeuwenhoek-Netherlands Cancer Institute, Amsterdam, the Netherlands
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35
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Seo M, Choi MHDORESMHCOMTCUOKSROKCSICESMHCOMTCUOKSROK, Lee YJ, Jung SE, Rha SE. Evaluating the added benefit of CT texture analysis on conventional CT analysis to differentiate benign ovarian cysts. Diagn Interv Radiol 2021; 27:460-468. [PMID: 34313229 DOI: 10.5152/dir.2021.20225] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
PURPOSE We aimed to evaluate the benefit of adding CT texture analysis on conventional CT features of benign adnexal cystic lesions, especially in identifying mucinous cystadenoma. METHODS This retrospective study included patients who underwent surgical removal of benign ovarian cysts (44 mucinous cystadenomas, 32 serous cystadenomas, 16 follicular/simple cysts and 43 endometriotic cysts) at our institution between January 2015 and November 2017. The CT images were independently reviewed by an abdominal radiologist (reviewer 1) and a resident (reviewer 2). Both reviewers recorded the conventional characteristics and performed texture analysis. Based on reviewer 1's results, two decision trees for differential diagnosis were developed. Reviewer 2's results were then applied to the decision trees. The diagnostic performances of each reviewer with and without the decision trees were compared. RESULTS Several conventional features and texture analysis parameters showed significant differences between mucinous cystadenomas and other benign adnexal cysts. The first decision tree selected septum number and thickness as significant features, whereas the second decision tree selected septum number and the mean values at spatial scaling factor (SSF) 0. Reviewer 1's performance did not change significantly with or without the use of the decision trees. Reviewer 2's interpretations were significantly less sensitive than reviewer 1's interpretations (p = 0.001). However, when aided by the first and second decision trees, Reviewer 2's interpretations were significantly more sensitive than reviewer 1's interpretations (86.4%, p < 0.001; 72.7%, p = 0.001). CONCLUSION This study suggests the benefit of CT texture analysis on conventional images to differentiate mucinous cystadenoma from other benign adnexal cysts, particularly for less experienced radiologists.
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Affiliation(s)
- Minkook Seo
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - Moon Hyung Department Of Radiology Eunpyeong St Mary's Hospital College Of Medicine The Catholic University Of Korea Seoul Republic Of Korea Catholic Smart Imaging Center Eunpyeong St Mary's Hospital College Of Medicine The Catholic University Of Korea Seoul Republic Of Korea Choi
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea;Catholic Smart Imaging Center, Eunpyeong St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - Young Joon Lee
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - Seung Eun Jung
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea;Catholic Smart Imaging Center, Eunpyeong St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - Sung Eun Rha
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
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36
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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] [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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Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma. AJR Am J Roentgenol 2021; 217:664-675. [PMID: 34259544 DOI: 10.2214/ajr.20.23195] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE. The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival (RFS) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS. This retrospective study enrolled 117 patients with HGSOC, including 90 patients with recurrence and 27 without recurrence; 1046 radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images using a manual segmentation method. L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select features, and the synthetic minority oversampling technique (SMOTE) was used to balance our dataset. A support vector machine (SVM) classifier was used to build the classification model. To validate the performance of the proposed models, we applied a leave-one-out cross-validation method to train and test the classifier. Cox proportional hazards regression, Harrell concordance index (C-index), and Kaplan-Meier plots analysis were used to evaluate the associations between radiomics signatures and RFS. RESULTS. The fusion radiomics-based model yielded a significantly higher AUC value of 0.85 in evaluating RFS than the model using contrast-enhanced T1-weighted imaging features alone or T2-weighted imaging features alone (AUC = 0.79 and 0.74 and p = .02 and .01, respectively). Kaplan-Meier survival curves showed significant differences between high and low recurrence risk in patients with HGSOC by different models. The fusion model combining radiomics features and clinical information showed higher performance than the clinical model (C-index = 0.62 and 0.60, respectively). CONCLUSION. The proposed MRI-based radiomics signatures may provide a potential way to develop a prediction model and can help identify patients with advanced HGSOC who have a high risk of recurrence.
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Manganaro L, Nicolino GM, Dolciami M, Martorana F, Stathis A, Colombo I, Rizzo S. Radiomics in cervical and endometrial cancer. Br J Radiol 2021; 94:20201314. [PMID: 34233456 PMCID: PMC9327743 DOI: 10.1259/bjr.20201314] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.
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Affiliation(s)
- Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Gabriele Maria Nicolino
- Post-graduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, Milan, Italy
| | - Miriam Dolciami
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Federica Martorana
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Anastasios Stathis
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland.,Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland
| | - Ilaria Colombo
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Stefania Rizzo
- Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland.,Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale, Via Tesserete 46, Lugano (CH), Switzerland
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Ai Y, Zhang J, Jin J, Zhang J, Zhu H, Jin X. Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors. Front Oncol 2021; 11:610742. [PMID: 34178617 PMCID: PMC8222738 DOI: 10.3389/fonc.2021.610742] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 04/28/2021] [Indexed: 11/17/2022] Open
Abstract
Background There is urgent need for an accurate preoperative prediction of metastatic status to optimize treatment for patients with ovarian cancer (OC). The feasibility of predicting the metastatic status based on radiomics features from preoperative computed tomography (CT) images alone or combined with clinical factors were investigated. Methods A total of 101 OC patients who underwent primary debulking surgery were enrolled. Radiomics features were extracted from the tumor volumes contoured on CT images with LIFEx package. Mann-Whitney U tests, least absolute shrinkage selection operator (LASSO), and Ridge Regression were applied to select features and to build prediction models. Univariate and regression analysis were applied to select clinical factors for metastatic prediction. The performance of models generated with radiomics features alone, clinical factors, and combined factors were evaluated and compared. Results Nine radiomics features were screened out from 184 extracted features to classify patients with and without metastasis. Age and cancer antigen 125 (CA125) were the two clinical factors that were associated with metastasis. The area under curves (AUCs) for the radiomics signature, clinical factors model, and combined model were 0.82 (95% CI, 0.66-0.98; sensitivity = 0.90, specificity = 0.70), 0.83 (95% CI, 0.67-0.95; sensitivity = 0.71, specificity = 0.8), and 0.86 (95% CI, 0.72-0.99, sensitivity = 0.81, specificity = 0.8), respectively. Conclusions Radiomics features alone or radiomics features combined with clinical factors are feasible and accurate enough to predict the metastatic status for OC patients.
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Affiliation(s)
- Yao Ai
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jindi Zhang
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haiyan Zhu
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Gynecology, Shanghai First Maternal and Infant Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiance Jin
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China
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Crombé A, Gauquelin L, Nougaret S, Chicart M, Pulido M, Floquet A, Guyon F, Croce S, Kind M, Cazeau AL. Diffusion-weighted MRI and PET/CT reproducibility in epithelial ovarian cancers during neoadjuvant chemotherapy. Diagn Interv Imaging 2021; 102:629-639. [PMID: 34112625 DOI: 10.1016/j.diii.2021.05.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/12/2023]
Abstract
PURPOSE To investigate the reproducibility of diffusion-weighted (DW) MRI and 18F-Fluorodeoxyglucose (18F-FDG)-Positron emission tomography/CT (PET/CT) in monitoring response to neoadjuvant chemotherapy in epithelial ovarian cancer. MATERIALS AND METHODS Ten women (median age, 67 years; range: 41.8-77.3 years) with stage IIIC-IV epithelial ovarian cancers were included in this prospective trial (NCT02792959) between 2014 and 2016. All underwent initial laparoscopic staging, four cycles of carboplatine-paclitaxel-based chemotherapy and interval debulking surgery. PET/CT and DW-MRI were performed at baseline (C0), after one cycle (C1) and before surgery (C4). Two nuclear physicians and two radiologists assessed five anatomic sites for the presence of ≥1 lesion. Target lesions in each site were defined and their apparent diffusion coefficient (ADC), maximal standardized uptake value (SUV-max), SUV-mean, SUL-peak, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were monitored (i.e., 10 patients ×5 sites ×3 time-points). Their relative early and late changes were calculated. Intra/inter-observer reproducibilities of qualitative and quantitative analysis were estimated with Kappa and intra-class correlation coefficients (ICCs). RESULTS For both modalities, inter- and intra-observer agreement percentages were excellent for initial staging but declined later for DW-MRI, leading to lower Kappa values for inter- and intra-observer variability (0.949 and 1 at C0, vs. 0.633 and 0.643 at C4, respectively) while Kappa values remained>0.8 for PET/CT. Inter- and intra-observer ICCs were>0.75 for SUV-max, SUL-peak, SUV-mean and their change regardless the time-point. ADC showed lower ICCs (range: 0.013-0.811). ANOVA found significant influences of the evaluation time, the measurement used (ADC, SUV-max, SUV-mean, SUV-max, SUL-peak, MTV or TLG) and their interaction on ICC values (P=0.0023, P<0.0001 and P =0.0028, respectively). CONCLUSION While both modalities demonstrated high reproducibility at baseline, only SUV-max, SUL-peak, SUV-mean and their changes maintained high reproducibility during chemotherapy.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France; Bordeaux University, 33000 Bordeaux, France; Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, 33405, Talence, France.
| | - Lisa Gauquelin
- Department of Biostatistics, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Stéphanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, University of Montpellier, 34090 Montpellier, France
| | - Marine Chicart
- Department of nuclear medicine, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Marina Pulido
- Department of Biostatistics, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Anne Floquet
- Department of Medical Oncology, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Frédéric Guyon
- Department of Oncological Surgery, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Sabrina Croce
- Department of Pathology, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
| | - Anne-Laure Cazeau
- Department of nuclear medicine, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 33000 Bordeaux, France
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Nougaret S, McCague C, Tibermacine H, Vargas HA, Rizzo S, Sala E. Radiomics and radiogenomics in ovarian cancer: a literature review. Abdom Radiol (NY) 2021; 46:2308-2322. [PMID: 33174120 DOI: 10.1007/s00261-020-02820-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/01/2020] [Accepted: 10/10/2020] [Indexed: 01/25/2023]
Abstract
Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.
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Affiliation(s)
- S Nougaret
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France. .,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France.
| | - Cathal McCague
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Hichem Tibermacine
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France.,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Stefania Rizzo
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, CH, Switzerland.,Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Lugano, CH, Switzerland
| | - E Sala
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
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Park H, Qin L, Guerra P, Bay CP, Shinagare AB. Decoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancy. Abdom Radiol (NY) 2021; 46:2376-2383. [PMID: 32728871 DOI: 10.1007/s00261-020-02668-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 07/11/2020] [Accepted: 07/17/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE To compare CT texture features of benign and malignant ovarian lesions and to build a machine learning model to detect malignancy in incidental ovarian lesions. METHODS In this IRB-approved, HIPAA-compliant, retrospective study, 427 consecutive patients with incidental ovarian lesions detected on contrast-enhanced CT (348, 81.5% benign and 79, 18.5% malignant) were included. The following CT texture features were analyzed using commercially available software (TexRAD, Feedback Plc, Cambridge, UK): total pixel, mean, standard deviation (SD), entropy, mean value of positive pixels (MPP), skewness, kurtosis and entropy. Three machine learning models were created by combining texture features and patients' age, and performance of these models was assessed using tenfold cross-validation. Receiver operating characteristics (ROC) were constructed to assess sensitivity and specificity. The cutoff value was picked using a cost-weighted method. RESULTS Total pixels, mean, SD, entropy, MPP, and skewness were significantly different between benign and malignant groups (p < 0.05). With a selected 10 as a cost factor to optimize cutoff value selection, sensitivity 92%, specificity 60% in the random forest (RF) model, sensitivity 91%, specificity 69% in SVM model, and sensitivity 92%, specificity 61% in the logistic regression, respectively. CONCLUSION CT texture analysis could provide objective imaging analysis of incidental ovarian lesions and ML models using CT texture features and age demonstrated high sensitivity and moderate specificity for detection of malignant lesions.
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Beer L, Martin-Gonzalez P, Delgado-Ortet M, Reinius M, Rundo L, Woitek R, Ursprung S, Escudero L, Sahin H, Funingana IG, Ang JE, Jimenez-Linan M, Lawton T, Phadke G, Davey S, Nguyen NQ, Markowetz F, Brenton JD, Crispin-Ortuzar M, Addley H, Sala E. Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer. Eur Radiol 2021; 31:3765-3772. [PMID: 33315123 PMCID: PMC8128813 DOI: 10.1007/s00330-020-07560-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 09/29/2020] [Accepted: 11/23/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To develop a precision tissue sampling technique that uses computed tomography (CT)-based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC). METHODS Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy. RESULTS We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7-30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53). CONCLUSION We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC. KEY POINTS • We developed a prevision tissue sampling technique that co-registers CT-based radiomics-based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).
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Affiliation(s)
- Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Marika Reinius
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Lorena Escudero
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Hilal Sahin
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Ionut-Gabriel Funingana
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Joo-Ern Ang
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | | | | | | | | | - Nghia Q Nguyen
- Information Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Helen Addley
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK.
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Michalet M, Azria D, Tardieu M, Tibermacine H, Nougaret S. Radiomics in radiation oncology for gynecological malignancies: a review of literature. Br J Radiol 2021; 94:20210032. [PMID: 33882246 DOI: 10.1259/bjr.20210032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.
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Affiliation(s)
- Morgan Michalet
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
| | - David Azria
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
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Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study. Acad Radiol 2021; 28:737-744. [PMID: 32229081 DOI: 10.1016/j.acra.2020.02.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 02/26/2020] [Accepted: 02/26/2020] [Indexed: 02/08/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability. MATERIALS AND METHODS Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings. RESULTS Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48). CONCLUSION We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.
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Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram. Eur Radiol 2021; 31:7855-7864. [PMID: 33864139 DOI: 10.1007/s00330-021-07902-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 02/13/2021] [Accepted: 03/16/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVES To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). METHODS In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. RESULTS In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). CONCLUSIONS The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.
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Tu SJ, Chen WY, Wu CT. Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging. Eur Radiol 2021; 31:7865-7875. [PMID: 33852047 DOI: 10.1007/s00330-021-07943-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/18/2021] [Accepted: 03/25/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images. METHODS A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty. RESULTS We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%. CONCLUSIONS Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent. KEY POINTS • Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample.
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Affiliation(s)
- Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, 259 Wen-Hua First Road, Kwei-Shan, Tao-Yuan, 333, Taiwan. .,Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan.
| | - Wei-Yuan Chen
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, 259 Wen-Hua First Road, Kwei-Shan, Tao-Yuan, 333, Taiwan.,Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
| | - Chen-Te Wu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, 259 Wen-Hua First Road, Kwei-Shan, Tao-Yuan, 333, Taiwan.,Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
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Wang X, Lu Z. Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer. Front Oncol 2021; 11:638124. [PMID: 33928029 PMCID: PMC8078590 DOI: 10.3389/fonc.2021.638124] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 03/16/2021] [Indexed: 01/23/2023] Open
Abstract
Objective To investigate radiomics features extracted from PET and CT components of 18F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC). Methods A total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts. Results In univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort. Conclusion PET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.
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Affiliation(s)
- Xihai Wang
- Department of Radiology, Shengjing Hospital, China Medical University, Shenyang, China
| | - Zaiming Lu
- Department of Radiology, Shengjing Hospital, China Medical University, Shenyang, China
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49
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Zhu H, Ai Y, Zhang J, Zhang J, Jin J, Xie C, Su H, Jin X. Preoperative Nomogram for Differentiation of Histological Subtypes in Ovarian Cancer Based on Computer Tomography Radiomics. Front Oncol 2021; 11:642892. [PMID: 33842352 PMCID: PMC8027335 DOI: 10.3389/fonc.2021.642892] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/03/2021] [Indexed: 12/27/2022] Open
Abstract
Objectives Non-invasive method to predict the histological subtypes preoperatively is essential for the overall management of ovarian cancer (OC). The feasibility of radiomics in the differentiating of epithelial ovarian cancer (EOC) and non-epithelial ovarian cancer (NEOC) based on computed tomography (CT) images was investigated. Methods Radiomics features were extracted from preoperative CT for 101 patients with pathologically proven OC. Radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) logistic regression. A nomogram was developed with the combination of radiomics features and clinical factors to differentiate EOC and NEOC. Results Eight radiomics features were selected to build a radiomics signature with an area under curve (AUC) of 0.781 (95% confidence interval (CI), 0.666 -0.897) in the discrimination between EOC and NEOC. The AUC of the combined model integrating clinical factors and radiomics features was 0.869 (95% CI, 0.783 -0.955). The nomogram demonstrated that the combined model provides a better net benefit to predict histological subtypes compared with radiomics signature and clinical factors alone when the threshold probability is within a range from 0.43 to 0.97. Conclusions Nomogram developed with CT radiomics signature and clinical factors is feasible to predict the histological subtypes preoperative for patients with OC.
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Affiliation(s)
- Haiyan Zhu
- Department of Gynecology, Shanghai First Maternal and Infant Hospital, Tongji University School of Medicine, Shanghai, China.,Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yao Ai
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jindi Zhang
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Congying Xie
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Radiation and Medical Oncology, The 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huafang Su
- Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiance Jin
- Department of Gynecology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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50
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Rizzo S, Manganaro L, Dolciami M, Gasparri ML, Papadia A, Del Grande F. Computed Tomography Based Radiomics as a Predictor of Survival in Ovarian Cancer Patients: A Systematic Review. Cancers (Basel) 2021; 13:cancers13030573. [PMID: 33540655 PMCID: PMC7867247 DOI: 10.3390/cancers13030573] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 01/27/2021] [Accepted: 01/29/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Ovarian cancer represents the most lethal gynecological malignancy. Since many new drugs have been recently introduced as adjunctive treatments for this pathology, an early prediction of outcome might be helpful to further improve outcomes. Radiomics represents a recent advancement, relying on extraction of quantitative features from imaging examinations. Indeed, clinical images, such as computed tomography images, may contain quantitative information, reflecting the underlying pathophysiology of a tumoral tissue. Radiomic analyses can be performed in tumor regions and metastatic lesions, as well as in normal tissues. The radiomic process relies on quantitative features, usually extracted by dedicated software, and then culminates in analysis and model building, according to a defined clinical question. This systematic review aims to evaluate association between radiomics based on computed tomography images and survival (in terms of overall survival and progression free survival) in ovarian cancer patients. Abstract The objective of this systematic review was to assess the results of radiomics for prediction of overall survival (OS) and progression free survival (PFS) in ovarian cancer (OC) patients. A secondary objective was to evaluate the findings of papers that based their analyses on inter-site heterogeneity. This systematic review was conducted according to the PRISMA statement. After the initial retrieval of 145 articles, the final systematic review comprised six articles. Association between radiomic features and OS was evaluated in 3/6 studies (50%); all articles showed a significant association between radiomic features and OS. Association with PFS was evaluated in 5/6 (83%) articles; the period of follow-up ranged between six and 36 months. All the articles showed significant association between radiomic models and PFS. Inter-site textural features were used for analysis in 2/6 (33%) articles. They demonstrated that high levels of inter-site textural heterogeneity were significantly associated with incomplete surgical resection in breast cancer gene-negative patients, and that lower heterogeneity was associated with complete resectability. There were some differences among papers in methodology; for example, only 3/6 (50%) articles included validation cohorts. In conclusion, radiomic models have demonstrated promising results as predictors of survival in OC patients, although larger studies are needed to allow clinical applicability.
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Affiliation(s)
- Stefania Rizzo
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- Correspondence: ; Tel.: +41-91-811-6676
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy; (L.M.); (M.D.)
| | - Miriam Dolciami
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy; (L.M.); (M.D.)
| | - Maria Luisa Gasparri
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- Department of Gynecology and Obstetrics, Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland
| | - Andrea Papadia
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
- Department of Gynecology and Obstetrics, Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland
| | - Filippo Del Grande
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Via Buffi 13, 6900 Lugano, Switzerland; (M.L.G.); (A.P.)
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