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Zhang C, Ma L, Zhao Y, Zhang Z, Zhang Q, Li X, Qin J, Ren Y, Hu Z, Zhao Q, Shen W, Cheng Y. Estimating pathological prognostic factors in epithelial ovarian cancers using apparent diffusion coefficients of functional tumor volume. Eur J Radiol 2024; 176:111514. [PMID: 38776804 DOI: 10.1016/j.ejrad.2024.111514] [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: 01/23/2024] [Revised: 04/26/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To assess the utility of apparent diffusion coefficients (ADCs) of whole tumor volume (WTV) and functional tumor volume (FTV) in determining the pathologicalprognostic factors in epithelial ovarian cancers (EOCs). METHODS A total of 155 consecutive patients who were diagnosed with EOC between January 2017 and August 2022 and underwent both conventional magnetic resonance imaging and diffusion-weighted imaging were assessed in this study. The maximum, minimum, and mean ADC values of the whole tumor (ADCwmax, ADCwmin, and ADCwmean, respectively) and functional tumor (ADCfmax, ADCfmin, and ADCfmean, respectively) as well as the WTV and FTV were derived from the ADC maps. The univariate and multivariate logistic regression analyses and receiver operating characteristic curve (ROC) analysis were used to assess the correlation between these ADC values and the pathological prognostic factors, namely subtypes, lymph node metastasis (LNM), Ki-67 index, and p53 expression. RESULTS The ADCfmean value was significantly lower in type II EOC, LNM-positive, and high-Ki-67 index groups compared to the type I EOC, LNM-negative, and low-Ki-67 index groups (p ≤ 0.001). Similarly, the ADCwmean and ADCfmean values were lower in the mutant-p53 group compared to the wild-type-p53 group (p ≤ 0.001). Additionally, the ADCfmean showed the highest area under the ROC curve (AUC) for evaluating type II EOC (0.725), LNM-positive (0.782), and high-Ki-67 index (0.688) samples among the given ROC curves, while both ADCwmean and ADCfmean showed high AUCs for assessing p53 expression (0.694 and 0.678, respectively). CONCLUSION The FTV-derived ADC values, especially ADCfmean, can be used to assess preoperative prognostic factors in EOCs.
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Affiliation(s)
- Cheng Zhang
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Luyang Ma
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Yujiao Zhao
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Zhijing Zhang
- School of Medicine, Nankai University, Tianjin, China.
| | - Qi Zhang
- The First Central Clinical School, Tianjin Medical University, Tianjin, China.
| | - Xiaotian Li
- School of Medicine, Nankai University, Tianjin, China.
| | - Jiaming Qin
- School of Medicine, Nankai University, Tianjin, China.
| | - Yan Ren
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Zhandong Hu
- Department of Pathology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Qian Zhao
- Department of Gynecology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
| | - Yue Cheng
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
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Wang Y, Lin W, Zhuang X, Wang X, He Y, Li L, Lyu G. Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review). Oncol Rep 2024; 51:46. [PMID: 38240090 PMCID: PMC10828921 DOI: 10.3892/or.2024.8705] [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: 07/18/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.
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Affiliation(s)
- Yanli Wang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Weihong Lin
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiaoling Zhuang
- Department of Pathology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiali Wang
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
| | - Yifang He
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Luhong Li
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Guorong Lyu
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
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Pisano G, Wendler T, Valdés Olmos RA, Garganese G, Rietbergen DDD, Giammarile F, Vidal-Sicart S, Oonk MHM, Frumovitz M, Abu-Rustum NR, Scambia G, Rufini V, Collarino A. Molecular image-guided surgery in gynaecological cancer: where do we stand? Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06604-1. [PMID: 38233609 DOI: 10.1007/s00259-024-06604-1] [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: 10/06/2023] [Accepted: 01/04/2024] [Indexed: 01/19/2024]
Abstract
PURPOSE The aim of this review is to give an overview of the current status of molecular image-guided surgery in gynaecological malignancies, from both clinical and technological points of view. METHODS A narrative approach was taken to describe the relevant literature, focusing on clinical applications of molecular image-guided surgery in gynaecology, preoperative imaging as surgical roadmap, and intraoperative devices. RESULTS The most common clinical application in gynaecology is sentinel node biopsy (SNB). Other promising approaches are receptor-target modalities and occult lesion localisation. Preoperative SPECT/CT and PET/CT permit a roadmap for adequate surgical planning. Intraoperative detection modalities span from 1D probes to 2D portable cameras and 3D freehand imaging. CONCLUSION After successful application of radio-guided SNB and SPECT, innovation is leaning towards hybrid modalities, such as hybrid tracer and fusion of imaging approaches including SPECT/CT and PET/CT. Robotic surgery, as well as augmented reality and virtual reality techniques, is leading to application of these innovative technologies to the clinical setting, guiding surgeons towards a precise, personalised, and minimally invasive approach.
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Affiliation(s)
- Giusi Pisano
- Section of Nuclear Medicine, University Department of Radiological Sciences and Haematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Thomas Wendler
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Augsburg, Germany
- Chair for Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Garching, Near Munich, Germany
| | - Renato A Valdés Olmos
- Interventional Molecular Imaging Laboratory & Section Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Giorgia Garganese
- Gynecologic Oncology Unit, Department of Women, Children and Public Health Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Daphne D D Rietbergen
- Interventional Molecular Imaging Laboratory & Section Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Francesco Giammarile
- Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria
| | - Sergi Vidal-Sicart
- Nuclear Medicine Department, Hospital Clinic Barcelona, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi iSunyer (IDIBAPS), Barcelona, Spain
| | - Maaike H M Oonk
- Department of Obstetrics and Gynaecology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Michael Frumovitz
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nadeem R Abu-Rustum
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Giovanni Scambia
- Gynecologic Oncology Unit, Department of Women, Children and Public Health Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Section of Obstetrics and Gynecology, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vittoria Rufini
- Section of Nuclear Medicine, University Department of Radiological Sciences and Haematology, Università Cattolica del Sacro Cuore, Rome, Italy
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Angela Collarino
- Nuclear Medicine Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
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Yang Y, Ye X, Zhou B, Liu Y, Feng M, Lv W, Lu D, Cui X, Liu J. Nomogram for predicting lymph node metastasis in patients with ovarian cancer using ultrasonography: a multicenter retrospective study. BMC Cancer 2023; 23:1121. [PMID: 37978453 PMCID: PMC10655276 DOI: 10.1186/s12885-023-11624-5] [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: 05/27/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Ovarian cancer is a common cancer among women globally, and the assessment of lymph node metastasis plays a crucial role in the treatment of this malignancy. The primary objective of our study was to identify the risk factors associated with lymph node metastasis in patients with ovarian cancer and develop a predictive model to aid in the selection of the appropriate surgical procedure and treatment strategy. METHODS We conducted a retrospective analysis of data from patients with ovarian cancer across three different medical centers between April 2014 and August 2022. Logistic regression analysis was employed to establish a prediction model for lymph node metastasis in patients with ovarian cancer. We evaluated the performance of the model using receiver operating characteristic (ROC) curves, calibration plots, and decision analysis curves. RESULTS Our analysis revealed that among the 368 patients in the training set, 101 patients (27.4%) had undergone lymph node metastasis. Maximum tumor diameter, multifocal tumor, and Ki67 level were identified as independent risk factors for lymph node metastasis. The area under the curve (AUC) of the ROC curve in the training set was 0.837 (95% confidence interval [CI]: 0.792-0.881); in the validation set this value was 0.814 (95% CI: 0.744-0.884). Calibration plots and decision analysis curves revealed good calibration and clinical application value. CONCLUSIONS We successfully developed a model for predicting lymph node metastasis in patients with ovarian cancer, based on ultrasound examination results and clinical data. Our model accurately identified patients at high risk of lymph node metastasis and may guide the selection of appropriate treatment strategies. This model has the potential to significantly enhance the precision and efficacy of clinical management in patients with ovarian cancer.
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Affiliation(s)
- Yaqin Yang
- Department of Ultrasound, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - Xuewei Ye
- Department of Ultrasound, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - Binqian Zhou
- Department of Ultrasound, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Liu
- Department of Ultrasound, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - Mei Feng
- Department of Ultrasound, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China
| | - Dan Lu
- Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Jianxin Liu
- Department of Ultrasound, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China.
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