1
|
Wu L, Li S, Li S, Lin Y, Wei D. Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1416378. [PMID: 39026971 PMCID: PMC11254676 DOI: 10.3389/fonc.2024.1416378] [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: 04/12/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024] Open
Abstract
Background The purpose of this systematic review and meta-analysis is to evaluate the potential significance of radiomics, derived from preoperative magnetic resonance imaging (MRI), in detecting deep stromal invasion (DOI), lymphatic vascular space invasion (LVSI) and lymph node metastasis (LNM) in cervical cancer (CC). Methods A rigorous and systematic evaluation was conducted on radiomics studies pertaining to CC, published in the PubMed database prior to March 2024. The area under the curve (AUC), sensitivity, and specificity of each study were separately extracted to evaluate the performance of preoperative MRI radiomics in predicting DOI, LVSI, and LNM of CC. Results A total of 4, 7, and 12 studies were included in the meta-analysis of DOI, LVSI, and LNM, respectively. The overall AUC, sensitivity, and specificity of preoperative MRI models in predicting DOI, LVSI, and LNM were 0.90, 0.83 (95% confidence interval [CI], 0.75-0.89) and 0.83 (95% CI, 0.74-0.90); 0.85, 0.80 (95% CI, 0.73-0.86) and 0.75 (95% CI, 0.66-0.82); 0.86, 0.79 (95% CI, 0.74-0.83) and 0.80 (95% CI, 0.77-0.83), respectively. Conclusion MRI radiomics has demonstrated considerable potential in predicting DOI, LVSI, and LNM in CC, positioning it as a valuable tool for preoperative precision evaluation in CC patients.
Collapse
Affiliation(s)
| | | | | | | | - Dayou Wei
- Department of Medical Ultrasound, Maoming People’s Hospital, Maoming, Guangdong, China
| |
Collapse
|
2
|
Liu J, Dong L, Zhang X, Wu Q, Yang Z, Zhang Y, Xu C, Wu Q, Wang M. Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer. Front Oncol 2024; 14:1376640. [PMID: 38779088 PMCID: PMC11109452 DOI: 10.3389/fonc.2024.1376640] [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: 02/15/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Background This study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods Patients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA). Results A total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively. Conclusions The pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.
Collapse
Affiliation(s)
- Jinjin Liu
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Linxiao Dong
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Xiaoxian Zhang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging, United Imaging Intelligence Co., Ltd., Beijing, China
| | - Zihan Yang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Yuejie Zhang
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Chunmiao Xu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Qingxia Wu
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Science, Zhengzhou, Henan, China
| |
Collapse
|
3
|
Shimizu H, Mori N, Mugikura S, Maekawa Y, Miyashita M, Nagasaka T, Sato S, Takase K. Application of Texture and Volume Model Analysis to Dedicated Axillary High-resolution 3D T2-weighted MR Imaging: A Novel Method for Diagnosing Lymph Node Metastasis in Patients with Clinically Node-negative Breast Cancer. Magn Reson Med Sci 2024; 23:161-170. [PMID: 36858636 PMCID: PMC11024718 DOI: 10.2463/mrms.mp.2022-0091] [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: 08/04/2022] [Accepted: 01/23/2023] [Indexed: 03/03/2023] Open
Abstract
PURPOSE To evaluate the effectiveness of the texture analysis of axillary high-resolution 3D T2-weighted imaging (T2WI) in distinguishing positive and negative lymph node (LN) metastasis in patients with clinically node-negative breast cancer. METHODS Between December 2017 and May 2021, 242 consecutive patients underwent high-resolution 3D T2WI and were classified into the training (n = 160) and validation cohorts (n = 82). We performed manual 3D segmentation of all visible LNs in axillary level I to extract the texture features. As the additional parameters, the number of the LNs and the total volume of all LNs for each case were calculated. The least absolute shrinkage and selection operator algorithm and Random Forest were used to construct the models. We constructed the texture model using the features from the LN with the largest least axis length in the training cohort. Furthermore, we constructed the 3 models combining the selected texture features of the LN with the largest least axis length, the number of LNs, and the total volume of all LNs: texture-number model, texture-volume model, and texture-number-volume model. As a conventional method, we manually measured the largest cortical diameter. Moreover, we performed the receiver operating curve analysis in the validation cohort and compared area under the curves (AUCs) of the models. RESULTS The AUCs of the texture model, texture-number model, texture-volume model, texture-number-volume model, and conventional method in the validation cohort were 0.7677, 0.7403, 0.8129, 0.7448, and 0.6851, respectively. The AUC of the texture-volume model was higher than those of other models and conventional method. The sensitivity, specificity, positive predictive value, and negative predictive value of the texture-volume model were 90%, 69%, 49%, and 96%, respectively. CONCLUSION The texture-volume model of high-resolution 3D T2WI effectively distinguished positive and negative LN metastasis for patients with clinically node-negative breast cancer.
Collapse
Affiliation(s)
- Hiroaki Shimizu
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Tohoku University School of Medicine, Sendai, Miyagi, Japan
| | - Naoko Mori
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Shunji Mugikura
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Division of Image Statistics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Yui Maekawa
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Minoru Miyashita
- Department of Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Tatsuo Nagasaka
- Department of Radiological Technology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Satoko Sato
- Department of Anatomic Pathology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Kei Takase
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| |
Collapse
|
4
|
Zhang XF, Wu HY, Liang XW, Chen JL, Li J, Zhang S, Liu Z. Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma. BMC Womens Health 2024; 24:182. [PMID: 38504245 PMCID: PMC10949581 DOI: 10.1186/s12905-024-03001-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 02/27/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).
Collapse
Grants
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
Collapse
Affiliation(s)
- Xue-Fang Zhang
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Hong-Yuan Wu
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Xu-Wei Liang
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Jia-Luo Chen
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Jianpeng Li
- Radiology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
| | - Shihao Zhang
- Pathology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
| | - Zhigang Liu
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
| |
Collapse
|
5
|
Xu Y, Li Z, Yang Y, Li L, Zhou Y, Ouyang J, Huang Z, Wang S, Xie L, Ye F, Zhou J, Ying J, Zhao H, Zhao X. A CT-based radiomics approach to predict intra-tumoral tertiary lymphoid structures and recurrence of intrahepatic cholangiocarcinoma. Insights Imaging 2023; 14:173. [PMID: 37840098 PMCID: PMC10577112 DOI: 10.1186/s13244-023-01527-1] [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: 07/04/2023] [Accepted: 09/13/2023] [Indexed: 10/17/2023] Open
Abstract
PURPOSE To predict the tertiary lymphoid structures (TLSs) status and recurrence-free survival (RFS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative CT radiomics. PATIENTS AND METHODS A total of 116 ICC patients were included (training: 86; external validation: 30). The enhanced CT images were performed for the radiomics model. The logistic regression analysis was applied for the clinical model. The combined model was based on the clinical and radiomics models. RESULTS A total of 107 radiomics features were extracted, and after being eliminated and selected, six features were combined to establish a radiomics model for TLSs prediction. Arterial phase diffuse hyperenhancement and AJCC 8th stage were combined to construct a clinical model. The combined (radiomics nomogram) model outperformed both the independent radiomics model and clinical model in the training cohort (AUC, 0.85 vs. 0.82 and 0.75, respectively) and was validated in the external validation cohort (AUC, 0.88 vs. 0.86 and 0.71, respectively). Patients in the rad-score no less than -0.76 (low-risk) group showed significantly better RFS than those in the less than -0.76 (high-risk) group (p < 0.001, C-index = 0.678). Patients in the nomogram score no less than -1.16 (low-risk) group showed significantly better RFS than those of the less than -1.16 (high-risk) group (p < 0.001, C-index = 0.723). CONCLUSIONS CT radiomics nomogram could serve as a preoperative biomarker of intra-tumoral TLSs status, better than independent radiomics or clinical models; preoperative CT radiomics nomogram achieved accurate stratification for RFS of ICC patients, better than the postoperative pathologic TLSs status. CRITICAL RELEVANCE STATEMENT The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models and better prognosis stratification than postoperative pathologic TLSs status in ICC patients, which may facilitate identifying patients benefiting most from surgery and subsequent immunotherapy. KEY POINTS • The combined (radiomics nomogram) model consisted of the radiomics model and clinical model (arterial phase diffuse hyperenhancement and AJCC 8th stage). • The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models in ICC patients. • Preoperative CT radiomics nomogram achieved more accurate stratification for RFS of ICC patients than the postoperative pathologic TLSs status.
Collapse
Affiliation(s)
- Ying Xu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuo Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yi Yang
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Key Laboratory of Gene Editing Screening and Research and Development (R&D) of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lu Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanzhao Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jingzhong Ouyang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhen Huang
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Key Laboratory of Gene Editing Screening and Research and Development (R&D) of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China
| | - Lizhi Xie
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China
| | - Feng Ye
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Jinxue Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China.
| | - Jianming Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Hong Zhao
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Key Laboratory of Gene Editing Screening and Research and Development (R&D) of Digestive System Tumor Drugs, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| |
Collapse
|
6
|
Bizzarri N, Russo L, Dolciami M, Zormpas-Petridis K, Boldrini L, Querleu D, Ferrandina G, Pedone Anchora L, Gui B, Sala E, Scambia G. Radiomics systematic review in cervical cancer: gynecological oncologists' perspective. Int J Gynecol Cancer 2023; 33:1522-1541. [PMID: 37714669 DOI: 10.1136/ijgc-2023-004589] [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] [Indexed: 09/17/2023] Open
Abstract
OBJECTIVE Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. METHODS A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. RESULTS A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. CONCLUSION Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.
Collapse
Affiliation(s)
- Nicolò Bizzarri
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Russo
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Miriam Dolciami
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Konstantinos Zormpas-Petridis
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Denis Querleu
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gabriella Ferrandina
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luigi Pedone Anchora
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evis Sala
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Scambia
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| |
Collapse
|
7
|
Liu S, Zhou Y, Wang C, Shen J, Zheng Y. Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images. BMC Med Imaging 2023; 23:101. [PMID: 37528338 PMCID: PMC10392004 DOI: 10.1186/s12880-023-01059-6] [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: 01/18/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images. METHODS A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models. RESULTS Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively. CONCLUSION The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.
Collapse
Affiliation(s)
- Shuyu Liu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Yu Zhou
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Caizhi Wang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Junjie Shen
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, 233004, China
| | - Yi Zheng
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China.
| |
Collapse
|
8
|
Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, Liu JG. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments? Mil Med Res 2023; 10:29. [PMID: 37357263 DOI: 10.1186/s40779-023-00464-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023] Open
Abstract
The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.
Collapse
Affiliation(s)
- Li-Tao Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhen-Yu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Wan-Fang Xie
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Li-Zhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Peking University, 100191, Beijing, China.
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
| | - Jian-Gang Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100029, China.
| |
Collapse
|
9
|
Zhang Z, Wan X, Lei X, Wu Y, Zhang J, Ai Y, Yu B, Liu X, Jin J, Xie C, Jin X. Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer. Insights Imaging 2023; 14:65. [PMID: 37060378 PMCID: PMC10105820 DOI: 10.1186/s13244-023-01405-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/16/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM. METHODS A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75). Radiomics features were extracted from both intra- and peritumoral regions with different expansion dimensions (3, 5, and 7 mm) in T2WI and DWI. Radiomics signature and combined radiomics models were constructed with selected features. A nomogram was also constructed by combining radiomics model with clinical factors for predicting LNM. RESULTS The area under curves (AUCs) of radiomics signature with features from tumors in T2WI and DWI were 0.841 vs. 0.791 and 0.820 vs. 0.771 in the training and testing sets, respectively. Combining radiomics features from tumors in the T2WI, DWI and peritumoral 3 mm expansion in T2WI achieved the best performance with an AUC of 0.868 and 0.846 in the training and testing sets, respectively. A nomogram combining age and maximum tumor diameter (MTD) with radiomics signature achieved a C-index of 0.884 in the prediction of LNM for ECC. CONCLUSIONS Radiomics features extracted from both intra- and peritumoral regions in T2WI and DWI are feasible and promising for the preoperative prediction of LNM for patients with ECC.
Collapse
Affiliation(s)
- Zhenhua Zhang
- Department of Radiology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaojie Wan
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiyao Lei
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yibo Wu
- Department of Radiotherapy Center, 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
| | - Yao Ai
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bing Yu
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xinmiao Liu
- School of Laboratory Medicine and Life Sciences, 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 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, 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.
| |
Collapse
|
10
|
Qian W, Chen Q, Hu C. Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis for Assessing Normal-Sized Lymph Node Metastasis in Cervical Cancer: Comparison Between Readout-Segmented and Single-Shot Echo-Planar Diffusion-Weighted Imaging. J Comput Assist Tomogr 2023; Publish Ahead of Print:00004728-990000000-00161. [PMID: 37380155 DOI: 10.1097/rct.0000000000001463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
OBJECTIVE To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer. METHODS Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADCmax, ADC90, ADCmedian, ADCmean, ADC10, ADCmin, ADCskewness, ADCkurtosis, and ADCentropy) was compared between SS-EPI and RS-EPI and between the 2 groups. RESULTS There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADCmax and ADCentropy but lower ADC10, ADCmin and ADCskewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC90 and higher ADCkurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC90 and higher ADCkurtosis and ADCentropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADCkurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%). CONCLUSIONS Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADCkurtosis held great potential in differentiating normal-sized LNM in cervical cancer.
Collapse
Affiliation(s)
| | - Qian Chen
- Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou City, Jiangsu Province, China
| | - Chunhong Hu
- From the Department of Radiology, the First Affiliated Hospital of Soochow University; and
| |
Collapse
|
11
|
Wang J, Mao Y, Gao X, Zhang Y. Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network. Front Oncol 2023; 13:1100087. [PMID: 36874136 PMCID: PMC9978213 DOI: 10.3389/fonc.2023.1100087] [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: 11/16/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023] Open
Abstract
Objectives Recurrence risk evaluation is clinically significant for patients with locally advanced cervical cancer (LACC). We investigated the ability of transformer network in recurrence risk stratification of LACC based on computed tomography (CT) and magnetic resonance (MR) images. Methods A total of 104 patients with pathologically diagnosed LACC between July 2017 and December 2021 were enrolled in this study. All patients underwent CT and MR scanning, and their recurrence status was identified by the biopsy. We randomly divided patients into training cohort (48 cases, non-recurrence: recurrence = 37: 11), validation cohort (21 cases, non-recurrence: recurrence = 16: 5), and testing cohort (35 cases, non-recurrence: recurrence = 27: 8), upon which we extracted 1989, 882 and 315 patches for model's development, validation and evaluation, respectively. The transformer network consisted of three modality fusion modules to extract multi-modality and multi-scale information, and a fully-connected module to perform recurrence risk prediction. The model's prediction performance was assessed by six metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, f1-score, sensitivity, specificity and precision. Univariate analysis with F-test and T-test were conducted for statistical analysis. Results The proposed transformer network is superior to conventional radiomics methods and other deep learning networks in both training, validation and testing cohorts. Particularly, in testing cohort, the transformer network achieved the highest AUC of 0.819 ± 0.038, while four conventional radiomics methods and two deep learning networks got the AUCs of 0.680 ± 0.050, 0.720 ± 0.068, 0.777 ± 0.048, 0.691 ± 0.103, 0.743 ± 0.022 and 0.733 ± 0.027, respectively. Conclusions The multi-modality transformer network showed promising performance in recurrence risk stratification of LACC and may be used as an effective tool to help clinicians make clinical decisions.
Collapse
Affiliation(s)
- Jian Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Yixiao Mao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Xinna Gao
- Department of Radiation Oncology, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Yu Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| |
Collapse
|
12
|
Qian W, Li Z, Chen W, Yin H, Zhang J, Xu J, Hu C. RESOLVE-DWI-based deep learning nomogram for prediction of normal-sized lymph node metastasis in cervical cancer: a preliminary study. BMC Med Imaging 2022; 22:221. [PMID: 36528577 PMCID: PMC9759891 DOI: 10.1186/s12880-022-00948-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) and related patient information to preoperatively predict normal-sized LNM in patients with cervical cancer. METHODS A dataset of MR images [RESOLVE-DWI and apparent diffusion coefficient (ADC)] and patient information (age, tumor size, International Federation of Gynecology and Obstetrics stage, ADC value and squamous cell carcinoma antigen level) of 169 patients with cervical cancer between November 2013 and January 2022 were retrospectively collected. The LNM status was determined by final histopathology. The collected studies were randomly divided into a development cohort (n = 126) and a test cohort (n = 43). A single-channel convolutional neural network (CNN) and a multi-channel CNN based on ResNeSt architectures were proposed for predicting normal-sized LNM from single or multi modalities of MR images, respectively. A DL nomogram was constructed by incorporating the clinical information and the multi-channel CNN. These models' performance was analyzed by the receiver operating characteristic analysis in the test cohort. RESULTS Compared to the single-channel CNN model using RESOLVE-DWI and ADC respectively, the multi-channel CNN model that integrating both two MR modalities showed improved performance in development cohort [AUC 0.848; 95% confidence interval (CI) 0.774-0.906] and test cohort (AUC 0.767; 95% CI 0.613-0.882). The DL nomogram showed the best performance in development cohort (AUC 0.890; 95% CI 0.821-0.938) and test cohort (AUC 0.844; 95% CI 0.701-0.936). CONCLUSION The DL nomogram incorporating RESOLVE-DWI and clinical information has the potential to preoperatively predict normal-sized LNM of cervical cancer.
Collapse
Affiliation(s)
- Weiliang Qian
- grid.429222.d0000 0004 1798 0228Department of Radiology, The First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, 215006 Jiangsu People’s Republic of China ,grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Zhisen Li
- grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Weidao Chen
- grid.507939.1Beijing Infervision Technology Co., Ltd, No.60 Dongsihuan Middle Road, Chaoyang District, Beijing, 100020 People’s Republic of China
| | - Hongkun Yin
- grid.507939.1Beijing Infervision Technology Co., Ltd, No.60 Dongsihuan Middle Road, Chaoyang District, Beijing, 100020 People’s Republic of China
| | - Jibin Zhang
- grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Jianming Xu
- grid.89957.3a0000 0000 9255 8984Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, No.26 Daoqian Street, Suzhou, 215002 Jiangsu People’s Republic of China
| | - Chunhong Hu
- grid.429222.d0000 0004 1798 0228Department of Radiology, The First Affiliated Hospital of Soochow University, No.188 Shizi Street, Suzhou, 215006 Jiangsu People’s Republic of China
| |
Collapse
|
13
|
de Alencar NRG, Machado MAD, Mourato FA, de Oliveira ML, Moraes TF, Mattos Junior LAR, Chang TMC, de Azevedo CRAS, Brandão SCS. Exploratory analysis of radiomic as prognostic biomarkers in 18F-FDG PET/CT scan in uterine cervical cancer. Front Med (Lausanne) 2022; 9:1046551. [PMID: 36569127 PMCID: PMC9769204 DOI: 10.3389/fmed.2022.1046551] [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: 11/10/2022] [Indexed: 12/05/2022] Open
Abstract
Objective To evaluate the performance of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET/CT) radiomic features to predict overall survival (OS) in patients with locally advanced uterine cervical carcinoma. Methods Longitudinal and retrospective study that evaluated 50 patients with cervical epidermoid carcinoma (clinical stage IB2 to IVA according to FIGO). Segmentation of the 18F-FDG PET/CT tumors was performed using the LIFEx software, generating the radiomic features. We used the Mann-Whitney test to select radiomic features associated with the clinical outcome (death), excluding the features highly correlated with each other with Spearman correlation. Subsequently, ROC curves and a Kaplan-Meier analysis were performed. A p-value < 0.05 were considered significant. Results The median follow-up was 23.5 months and longer than 24 months in all surviving patients. Independent predictors for OS were found-SUVpeak with an AUC of 0.74, sensitivity of 77.8%, and specificity of 72.7% (p = 0.006); and the textural feature gray-level run-length matrix GLRLM_LRLGE, with AUC of 0.74, sensitivity of 72.2%, and specificity of 81.8% (p = 0.005). When we used the derived cut-off points from these ROC curves (12.76 for SUVpeak and 0.001 for GLRLM_LRLGE) in a Kaplan-Meier analysis, we can see two different groups (one with an overall survival probability of approximately 90% and the other with 30%). These biomarkers are independent of FIGO staging. Conclusion By radiomic 18F-FDG PET/CT data analysis, SUVpeak and GLRLM_LRLGE textural feature presented the best performance to predict OS in patients with cervical cancer undergoing chemo-radiotherapy and brachytherapy.
Collapse
Affiliation(s)
- Nadja Rolim Gonçalves de Alencar
- Master of Science Surgery Post-Graduation Program, Federal University of Pernambuco, Recife, Pernambuco, Brazil,Department of Radiology and Nuclear Medicine, Hospital das Clínicas, Federal University of Pernambuco, Recife, Pernambuco, Brazil,*Correspondence: Nadja Rolim Gonçalves de Alencar,
| | - Marcos Antônio Dórea Machado
- Department of Radiology, Complexo Hospitalar Universitário Professor Edgard Santos/Universidade Federal da Bahia (UFBA), Salvador, Bahia, Brazil
| | - Felipe Alves Mourato
- Master of Science Surgery Post-Graduation Program, Federal University of Pernambuco, Recife, Pernambuco, Brazil,Department of Radiology and Nuclear Medicine, Hospital das Clínicas, Federal University of Pernambuco, Recife, Pernambuco, Brazil
| | | | | | | | - Tien-Man Cabral Chang
- Nuclear Medicine Service, Instituto de Medicina Integrada Fernandes Figueira, Recife, Pernambuco, Brazil
| | | | - Simone Cristina Soares Brandão
- Master of Science Surgery Post-Graduation Program, Federal University of Pernambuco, Recife, Pernambuco, Brazil,Department of Radiology and Nuclear Medicine, Hospital das Clínicas, Federal University of Pernambuco, Recife, Pernambuco, Brazil,Clinical Medicine, Center for Medical Sciences, Federal University of Pernambuco, Recife, Pernambuco, Brazil
| |
Collapse
|
14
|
Liu X, Tian J, Wu J, Zhang Y, Wang X, Zhang X, Wang X. Utility of diffusion weighted imaging-based radiomics nomogram to predict pelvic lymph nodes metastasis in prostate cancer. BMC Med Imaging 2022; 22:190. [DOI: 10.1186/s12880-022-00905-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022] Open
Abstract
Abstract
Background
Preoperative pelvic lymph node metastasis (PLNM) prediction can help clinicians determine whether to perform pelvic lymph node dissection (PLND). The purpose of this research is to explore the feasibility of diffusion-weighted imaging (DWI)-based radiomics for preoperative PLNM prediction in PCa patients at the nodal level.
Methods
The preoperative MR images of 1116 pathologically confirmed lymph nodes (LNs) from 84 PCa patients were enrolled. The subjects were divided into a primary cohort (67 patients with 192 positive and 716 negative LNs) and a held-out cohort (17 patients with 43 positive and 165 negative LNs) at a 4:1 ratio. Two preoperative pelvic lymph node metastasis (PLNM) prediction models were constructed based on automatic LN segmentation with quantitative radiological LN features alone (Model 1) and combining radiological and radiomics features (Model 2) via multiple logistic regression. The visual assessments of junior (Model 3) and senior (Model 4) radiologists were compared.
Results
No significant difference was found between the area under the curve (AUCs) of Models 1 and 2 (0.89 vs. 0.90; P = 0.573) in the held-out cohort. Model 2 showed the highest AUC (0.83, 95% CI 0.76, 0.89) for PLNM prediction in the LN subgroup with a short diameter ≤ 10 mm compared with Model 1 (0.78, 95% CI 0.70, 0.84), Model 3 (0.66, 95% CI 0.52, 0.77), and Model 4 (0.74, 95% CI 0.66, 0.88). The nomograms of Models 1 and 2 yielded C-index values of 0.804 and 0.910, respectively, in the held-out cohort. The C-index of the nomogram analysis (0.91) and decision curve analysis (DCA) curves confirmed the clinical usefulness and benefit of Model 2.
Conclusions
A DWI-based radiomics nomogram incorporating the LN radiomics signature with quantitative radiological features is promising for PLNM prediction in PCa patients, particularly for normal-sized LNM.
Collapse
|
15
|
Li Q, Song Z, Zhang D, Li X, Liu Q, Yu J, Li Z, Zhang J, Ren X, Wen Y, Tang Z. Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients. Front Oncol 2022; 12:992906. [PMID: 36276058 PMCID: PMC9579427 DOI: 10.3389/fonc.2022.992906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/20/2022] [Indexed: 12/02/2022] Open
Abstract
Objectives To investigate the potential value of a contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining a lymph node (LN) radiomics signature and LNs’ radiological features for preoperative detection of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, 196 LNs in 61 PDAC patients were enrolled and divided into the training (137 LNs) and validation (59 LNs) cohorts. Radiomic features were extracted from portal venous phase images of LNs. The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to select optimal features to determine the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis by multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by receiver operating characteristic curve (ROC), decision curve (DCA) and calibration curve analyses. Results The radiological model, including LN size, and margin and enhancement pattern (three significant predictors), exhibited areas under the curves (AUCs) of 0.831 and 0.756 in the training and validation cohorts, respectively. Nine radiomic features were used to construct a radiomics model, which showed AUCs of 0.879 and 0.804 in the training and validation cohorts, respectively. The radiological-radiomics nomogram, which incorporated the LN Rad-score and the three LNs’ radiological features, performed better than the Rad-score and radiological models individually, with AUCs of 0.937 and 0.851 in the training and validation cohorts, respectively. Calibration curve analysis and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the highest net benefit for preoperative diagnosis of LN metastasis. Conclusions The CT-based LN radiological-radiomics nomogram may serve as a valid and convenient computer-aided tool for personalized risk assessment of LN metastasis and help clinicians make appropriate clinical decisions for PADC patients.
Collapse
Affiliation(s)
- Qian Li
- Department of Radiology, Chongqing Medical University, Chongqing, China
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Zuhua Song
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Dan Zhang
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Xiaojiao Li
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Qian Liu
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Jiayi Yu
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Zongwen Li
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Jiayan Zhang
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Xiaofang Ren
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Youjia Wen
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Zhuoyue Tang
- Department of Radiology, Chongqing Medical University, Chongqing, China
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
- Department of Radiology, Chongqing General Hospital, Chongqing, China
- *Correspondence: Zhuoyue Tang,
| |
Collapse
|
16
|
Hu Q, Shi J, Zhang A, Duan S, Song J, Chen T. Added value of radiomics analysis in MRI invisible early-stage cervical cancers. Br J Radiol 2022; 95:20210986. [PMID: 35143254 PMCID: PMC10993977 DOI: 10.1259/bjr.20210986] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/09/2022] [Accepted: 01/25/2022] [Indexed: 01/19/2023] Open
Abstract
OBJECTIVES To determine the diagnostic ability of cervical mucosa radiomics signature of sagittal T2WI and T1 contrast-enhanced (CE) imaging in detecting early-stage cervical cancers with negative MRI. METHODS Preoperative images of postoperative pathology confirmed early-stage cervical cancer patients and normal cervix patients admitted to our hospital between January 2013 and December 2020 were retrospectively reviewed. Patients with cancer signals on T2WI, T1CE and DWI were deleted. Regions of interests (ROIs) were delineated on cervical mucosa (from cervical canal to cervical dome) with 5 mm width on sagittal T2WI and T1CE. The maximum-relevance and minimumredundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the calculation of radiomics signature scores. Diagnostic performance was assessed and compared between radiomics prediction models (model 1: T1CE; model 2: T2WI; model 3: model one combined with model 2). Differential diagnostic ability of radiomics signature in detecting lymphatic vascular space invasion (LVSI) was further explored. RESULTS Diagnostic performance of model three was higher than model 1 and model 2 both in primary (model 3 0.874, model 1 0.857, model 2 0.816) and validation (model 3 0.853, model 1 0.847, model 2 0.634) cohorts. Model 3 showed statistical diagnostic difference compared with model 2 (primary p = 0.008, validation p = 0.000). However, the diagnostic improvement ability of model 3 showed no statistical difference compared with model 1 (primary p = 0.351, validation p = 0.739). Diagnostic efficiency of model 3 in detecting LVSI was not apparent (AUC 0.64). CONCLUSIONS Radiomics analysis of cervical mucosa combining T1CE and T2WI is promising for predicting MRI invisible early-stage cervical cancers, however further ability in detecting LVSI was not apparent. ADVANCES IN KNOWLEDGE Conventional MRI was originally defined as meaningless in very early-stage cervical cancers. However, whether MRI radiomics analysis of cervical mucosa can detecting tiny changes of invisible early stage cervical cancers has not been researched yet.
Collapse
Affiliation(s)
- Qiming Hu
- Department of Obstetrics & Gynecology, the First Affiliated
Hospital of Nanjing Medical University,
Nanjing, China
| | - Jinming Shi
- Department of Radiology, the First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Aining Zhang
- Department of Radiology, the First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution,
Shanghai, China
| | - Jiacheng Song
- Department of Radiology, the First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Ting Chen
- Department of Radiology, the First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| |
Collapse
|
17
|
Li L, Zhang J, Zhe X, Tang M, Zhang X, Lei X, Zhang L. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. Eur J Radiol 2022; 151:110243. [DOI: 10.1016/j.ejrad.2022.110243] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/22/2022] [Accepted: 03/05/2022] [Indexed: 12/23/2022]
|
18
|
Liu B, Sun Z, Xu ZL, Zhao HL, Wen DD, Li YA, Zhang F, Hou BX, Huan Y, Wei LC, Zheng MW. Predicting Disease-Free Survival With Multiparametric MRI-Derived Radiomic Signature in Cervical Cancer Patients Underwent CCRT. Front Oncol 2022; 11:812993. [PMID: 35145910 PMCID: PMC8821662 DOI: 10.3389/fonc.2021.812993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT).
Collapse
Affiliation(s)
- Bing Liu
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Zhen Sun
- Department of Orthopaedics, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Zi-Liang Xu
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Hong-Liang Zhao
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Di-Di Wen
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Yong-Ai Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Fan Zhang
- Department of Radiology, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Bing-Xin Hou
- Department of Radiation Oncology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
- *Correspondence: Yi Huan, ; Li-Chun Wei, ; Min-Wen Zheng,
| | - Li-Chun Wei
- Department of Radiation Oncology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
- *Correspondence: Yi Huan, ; Li-Chun Wei, ; Min-Wen Zheng,
| | - Min-Wen Zheng
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
- *Correspondence: Yi Huan, ; Li-Chun Wei, ; Min-Wen Zheng,
| |
Collapse
|
19
|
Ran C, Sun J, Qu Y, Long N. Clinical value of MRI, serum SCCA, and CA125 levels in the diagnosis of lymph node metastasis and para-uterine infiltration in cervical cancer. World J Surg Oncol 2021; 19:343. [PMID: 34886853 PMCID: PMC8656033 DOI: 10.1186/s12957-021-02448-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/13/2021] [Indexed: 12/13/2022] Open
Abstract
Background Cervical cancer shows great differences in depth of invasion, metastasis, and other biological behaviors. The location of the lesion is special, so it is usually difficult to determine the clinical stage. This study aimed to explore the clinical value of magnetic resonance imaging (MRI) and tumor serum markers for the preoperative diagnosis of cervical cancer lymph node metastasis and para-uterine invasion. Methods A total of 200 patients with cervical cancer admitted to our hospital from January 2019 to January 2020 were collected as the research subjects. Comparing the diagnosis results of preoperative MRI scan, serum tumor markers, and postoperative pathological examination using single factor comparison, we determined the MRI scan results, the comprehensive matching rate between serum tumor markers (squamous cell carcinoma antigen (SCCA), carbohydrate antigen 125 (CA125)) and postoperative pathological results, and the differences of sensitivity, specificity, and accuracy in the prediction of lymph node metastasis and para-uterine infiltration of cervical cancer. Results The levels of SCCA and CA125 in patients with para-uterine invasion and lymph node metastasis were higher than those of patients without invasion and metastasis. Among them, the level of SCCA was significantly different (P<0.05). The level of CA125 was not statistically significant (P>0.05), so MRI combined with serum SCCA was selected for combined diagnosis in the later period. The sensitivity, specificity, and accuracy of MRI diagnosis of cervical cancer and para-uterine infiltrating lymph node metastasis and metastasis were 55.2, 91.6, and 89.5% and 55.2, 91.6, and 89.5%, respectively. These data in MRI combined with serum SCCA were 76.3, 95.3, and 94.3% and 63.2, 96.0, and 95.1%, respectively. The accuracy of tumor markers combined with MRI in the diagnosis of cervical cancer lymph node metastasis and para-uterine invasion was higher than that of MRI. Conclusions MRI combined with serum SCCA can more accurately identify cervical cancer lymph node metastasis and para-uterine invasion compared with MRI alone. Tumor marker combined with MRI diagnosis is an important auxiliary method for cervical cancer treatment and can provide comprehensive and reliable clinical evidence for evaluation before cervical cancer surgery.
Collapse
Affiliation(s)
- Chao Ran
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China
| | - Jian Sun
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China
| | - Yunhui Qu
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China
| | - Na Long
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China.
| |
Collapse
|
20
|
Ren H, Mori N, Mugikura S, Shimizu H, Kageyama S, Saito M, Takase K. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging. Abdom Radiol (NY) 2021; 46:5344-5352. [PMID: 34331104 DOI: 10.1007/s00261-021-03226-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/20/2021] [Accepted: 07/20/2021] [Indexed: 01/01/2023]
Abstract
PURPOSE To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS). METHODS Eighty consecutive patients (normal group, n = 50; PAS group, n = 30) underwent preoperative MRI. A scoring system (0-2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance. RESULTS The AUC of visual analysis was 0.75. The model 'coronal and sagittal' had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model 'coronal and sagittal' showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model 'coronal and sagittal' for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75; p < 0.0001). CONCLUSION The model 'coronal and sagittal' can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS.
Collapse
|
21
|
Li H, Zhu M, Jian L, Bi F, Zhang X, Fang C, Wang Y, Wang J, Wu N, Yu X. Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer. Front Oncol 2021; 11:706043. [PMID: 34485139 PMCID: PMC8415417 DOI: 10.3389/fonc.2021.706043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/19/2021] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVES Accurate prediction of prognosis will help adjust or optimize the treatment of cervical cancer and benefit the patients. We aimed to investigate the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer. METHODS This retrospective study included 106 patients with cervical cancer (FIGO stage IB1-IVa) between October 2017 and May 2019. Patients were randomly divided into a training cohort (n = 74) and validation cohort (n = 32). All patients underwent contrast-enhanced computed tomography (CT) prior to treatment. The ITK-SNAP software was used to delineate the region of interest on pre-treatment standard-of-care CT scans. We extracted 792 two-dimensional radiomic features by the Analysis Kit (AK) software. Pearson correlation coefficient analysis and Relief were used to detect the most discriminatory features. The radiomic signature (i.e., Radscore) was constructed via Adaboost with Leave-one-out cross-validation. Prognostic models were built by Cox regression model using Akaike information criterion (AIC) as the stopping rule. A nomogram was established to individually predict the OS of patients. Patients were then stratified into high- and low-risk groups according to the Youden index. Kaplan-Meier curves were used to compare the survival difference between the high- and low-risk groups. RESULTS Six textural features were identified, including one gray-level co-occurrence matrix feature and five gray-level run-length matrix features. Only the FIGO stage and Radscore were independent risk factors associated with OS (p < 0.05). The C-index of the FIGO stage in the training and validation cohorts was 0.703 (95% CI: 0.572-0.834) and 0.700 (95% CI: 0.526-0.874), respectively. Correspondingly, the C-index of Radscore was 0.794 (95% CI: 0.707-0.880) and 0.754 (95% CI: 0.623-0.885). The incorporation of the FIGO stage and Radscore achieved better performance, with a C-index of 0.830 (95% CI: 0.738-0.922) and 0.772 (95% CI: 0.615-0.929), respectively. The nomogram based on the FIGO stage and Radscore could individually predict the OS probability with good discrimination and calibration. The high-risk patients had shorter OS compared with the low-risk patients (p < 0.05). CONCLUSION Radiomics has the potential for noninvasive risk stratification and may improve the prediction of OS in patients with cervical cancer when added to the FIGO stage.
Collapse
Affiliation(s)
- Handong Li
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Miaochen Zhu
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lian Jian
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Feng Bi
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoye Zhang
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Chao Fang
- Department of Clinical Pharmaceutical Research Institution, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, China
| | - Ying Wang
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Jing Wang
- Gynecological Oncology Clinical Research Center, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, China
| | - Nayiyuan Wu
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoping Yu
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| |
Collapse
|