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Lu M, Wang C, Zhuo Y, Gou J, Li Y, Li J, Dong X. Preoperative prediction power of radiomics and non-radiomics methods based on MRI for early recurrence in hepatocellular carcinoma: a systemic review and meta-analysis. Abdom Radiol (NY) 2024:10.1007/s00261-024-04356-y. [PMID: 38704783 DOI: 10.1007/s00261-024-04356-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: 02/28/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE To compare radiomics and non-radiomics in predicting early recurrence (ER) in patients with hepatocellular carcinoma (HCC) after curative surgery. METHODS We systematically searched PubMed and Embase databases. Studies with clear reference criteria were selected. Data were extracted and assessed for quality using the quality in prognosis studies tool (QUIPS) by two independent authors. All included radiomics studies underwent radiomics quality score (RQS) assessment. We calculated sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) using random or fixed models with a 95%CI. Forest maps visualized the data, and summary receiver operating characteristic (sROC) curves with the area under the curve (AUC) were generated. Meta-regression and subgroup analyses explored sources of heterogeneity. We compared sensitivity, specificity, PLR, and NLR using the z-test and compared AUC values using the Delong test. RESULTS Our meta-analysis included 10 studies comprising 1857 patients. For radiomics, the pooled sensitivity, specificity, AUC of sROC, PLR and NLR were 0.84(95%CI: 0.78-0.89), 0.80(95%CI: 0.75-0.85), 0.89(95%CI: 0.86-0.91), 4.28(95%CI: 3.48-5.27) and 0.20(95%CI: 0.14-0.27), respectively, but with significant heterogeneity (I2 = 60.78% for sensitivity, I2 = 55.79% for specificity) and potential publication bias (P = 0.04). The pooled sensitivity, specificity, AUC of sROC, PLR, NLR for non-radiomics were 0.75(95%CI:0.68-0.81), 0.78(95%CI:0.72-0.83), 0.83(95%CI: 0.80-0.86), 3.45(95%CI: 2.68-4.44) and 0.32(95%CI: 0.24-0.41), respectively. There was no significant heterogeneity in this group (I2 = 0% for sensitivity, I2 = 17.27% for specificity). Radiomics showed higher diagnostic accuracy (AUC: 0.89 vs. 0.83, P = 0.0456), higher sensitivity (0.84 vs. 0.75, P = 0.0385) and lower NLR (0.20 vs. 0.32, P = 0.0287). CONCLUSION The radiomics from preoperative MRI effectively predicts ER of HCC and has higher diagnostic accuracy than non-radiomics. Due to potential publication bias and suboptimal RQS scores in radiomics, these results should be interpreted cautiously.
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Affiliation(s)
- Mingjie Lu
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Chen Wang
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Yi Zhuo
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Junjiu Gou
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Yingfeng Li
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Jingqi Li
- The Clinical Medical College, Guizhou Province, Guizhou Medical University, Guiyang, 550004, China
| | - Xue Dong
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
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Xia T, Zhao B, Li B, Lei Y, Song Y, Wang Y, Tang T, Ju S. MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges. J Magn Reson Imaging 2024; 59:767-783. [PMID: 37647155 DOI: 10.1002/jmri.28982] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ben Zhao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Binrong Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ying Lei
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Yuancheng Wang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyu Tang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Zhang L, Chen J, Lai X, Zhang X, Xu J. Dual-phenotype hepatocellular carcinoma: correlation of MRI features with other primary hepatocellular carcinoma and differential diagnosis. Front Oncol 2024; 13:1253873. [PMID: 38273849 PMCID: PMC10808764 DOI: 10.3389/fonc.2023.1253873] [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: 07/06/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Objectives Dual-phenotype hepatocellular carcinoma (DPHCC) is a rare subtype of hepatocellular carcinoma characterized by high invasiveness and a poor prognosis. The study aimed to compare clinical and magnetic resonance imaging (MRI) features of DPHCC with that of non-DPHCC and intrahepatic cholangiocarcinoma (ICC), exploring the most valuable features for diagnosing DPHCC. Methods A total of 208 cases of primary liver cancer, comprising 27 DPHCC, 113 non-DPHCC, and 68 ICC, who undergone gadoxetic acid-enhanced MRI, were enrolled in this study. The clinicopathologic and MRI features of all cases were summarized and analyzed. Univariate and multivariate logistic regression analyses were conducted to identify the predictors. Kaplan-Meier survival analysis was used to evaluate the 1-year and 2-year disease-free survival (DFS) and overall survival (OS) rates in the cohorts. Results In the multivariate analysis, the absence of tumor capsule (P = 0.046; OR = 9.777), persistent enhancement (P = 0.006; OR = 46.941), arterial rim enhancement (P = 0.011; OR = 38.211), and target sign on DWI image (P = 0.021; OR = 30.566) were identified as independently significant factors for distinguishing DPHCC from non-DPHCC. Serum alpha-fetoprotein (AFP) >20 μg/L (P = 0.036; OR = 67.097) and hepatitis B virus (HBV) positive (P = 0.020; OR = 153.633) were independent significant factors for predicting DPHCC compared to ICC. The 1-year and 2-year DFS rates for patients in the DPHCC group were 65% and 50%, respectively, whereas those for the non-DPHCC group were 80% and 60% and for the ICC group were 50% and 29%, respectively. The 1-year and 2-year OS rates for patients in the DPHCC group were 74% and 60%, respectively, whereas those for the non-DPHCC group were 87% and 70% and for the ICC group were 55% and 37%, respectively. Kaplan-Meier survival analysis revealed significant differences in the 1-year and 2-year OS rates between the DPHCC and non-DPHCC groups (P = 0.030 and 0.027) as well as between the DPHCC and ICC groups (P = 0.029 and 0.016). Conclusion In multi-parameter MRI, combining the assessment of the absence of tumor capsule, persistent enhancement, arterial rim enhancement, and target sign on DWI image with clinical data such as AFP >20 μg/L and HBV status may support in the diagnosis of DPHCC and differentiation from non-DPHCC and ICC. Accurate preoperative diagnosis facilitates the selection of personalized treatment options.
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Affiliation(s)
- Liqing Zhang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China
| | - Jing Chen
- Department of Radiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
| | - Xufeng Lai
- Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China
| | - Xiaoqian Zhang
- Department of Radiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
| | - Jianfeng Xu
- Department of Radiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
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Gu HX, Huang XS, Xu JX, Zhu P, Xu JF, Fan SF. Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study. J Digit Imaging 2023; 36:2554-2566. [PMID: 37578576 PMCID: PMC10584802 DOI: 10.1007/s10278-023-00888-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/15/2023] Open
Abstract
This study aimed to explore the magnetic resonance imaging (MRI) features of dual-phenotype hepatocellular carcinoma (DPHCC) and their diagnostic value.The data of 208 patients with primary liver cancer were retrospectively analysed between January 2016 and June 2021. Based on the pathological diagnostic criteria, 27 patients were classified into the DPHCC group, 113 patients into the noncholangiocyte-phenotype hepatocellular carcinoma (NCPHCC) group, and 68 patients with intrahepatic cholangiocarcinoma (ICC) were classified into the ICC group. Two abdominal radiologists reviewed the preoperative MRI features by a double-blind method. The MRI features and key laboratory and clinical indicators were compared between the groups. The potentially valuable MRI features and key laboratory and clinical characteristics for predicting DPHCC were identified by univariate and multivariate analyses, and the odds ratios (ORs) were recorded. In multivariate analysis, tumour without capsule (P = 0.046, OR = 9.777), dynamic persistent enhancement (P = 0.006, OR = 46.941), and targetoid appearance on diffusion-weighted imaging (DWI) (P = 0.021, OR = 30.566) were independently significant factors in the detection of DPHCC compared to NCPHCC. Serum alpha-fetoprotein (AFP) > 20 µg/L (P = 0.036, OR = 67.097) and prevalence of hepatitis B virus (HBV) infection (P = 0.020, OR = 153.633) were independent significant factors in predicting DPHCC compared to ICC. The differences in other tumour marker levels and imaging features between the groups were not significant. In MR enhanced and diffusion imaging, tumour without capsule, persistent enhancement and DWI targetoid findings, combined with AFP > 20 µg/L and HBV infection-positive laboratory results, can help to diagnose DPHCC and differentiate it from NCPHCC and ICC. These results suggest that clinical, laboratory and MRI features should be integrated to construct an AI diagnostic model for DPHCC.
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Affiliation(s)
- Hong-Xian Gu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
- Department of Radiology, the People's Hospital of Jianyang City, Chengdu, 641499, China
| | - Xiao-Shan Huang
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Jian-Xia Xu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Ping Zhu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Jian-Feng Xu
- Department of Radiology, Shulan (Hangzhou) Hospital, Hangzhou, 310000, China.
| | - Shu-Feng Fan
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China.
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Liu MT, Zhang JY, Xu L, Qu Q, Lu MT, Jiang JF, Zhao XC, Zhang XQ, Zhang T. A multivariate model based on gadoxetic acid-enhanced MRI using Li-RADS v2018 and other imaging features for preoperative prediction of dual‑phenotype hepatocellular carcinoma. LA RADIOLOGIA MEDICA 2023; 128:1333-1346. [PMID: 37740839 DOI: 10.1007/s11547-023-01715-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/25/2023] [Indexed: 09/25/2023]
Abstract
OBJECTIVE To investigate the diagnostic value of liver imaging reporting and data system (LI-RADS) v2018 and other imaging features in dual-phenotype hepatocellular carcinoma (DPHCC), establish a prediagnostic model based on gadoxetic acid-enhanced MRI, and explore the prognostic significance after surgery of the DPHCC. MATERIALS AND METHODS Preoperative enhanced MRI findings and the clinical and pathological data of patients with surgically confirmed HCC were analysed retrospectively. Image analysis was based on LI-RADS v2018 and other image features. Univariate analysis was used to screen for predictive factors of DPHCC, and multivariate logistic regression analysis was used to determine the predictive factors. A regression diagnostic model was established. Receiver operating characteristic (ROC) curve analysis was used to determine the critical value, area under curve (AUC), and the corresponding 95% confidence interval (95% CI). The diagnostic performance was verified by fivefold cross-validation. Cox regression analysis was used to determine the prognostic factors associated with early recurrence after surgical resection. RESULTS In total, 158 patients were included, of whom 79 had DPHCC and 79 had non-DPHCC. Multivariate analysis showed that rim arterial phase hyperenhancement (Rim APHE) and targetoid restriction were independent risk factors for DPHCC (P < 0.05). The AUC (95% CI) of the model was 0.862 (0.807-0.918), sensitivity was 81.01%, and specificity was 89.874%. Cox regression analysis showed that DPHCC, microvascular invasion, tumour diameter, and an increase of alpha-fetoprotein were independent factors for recurrence. CONCLUSION Rim APHE and targetoid restriction were sensitive imaging features of DPHCC before surgery, and the identification of DPHCC has important prognostic significance for early recurrence.
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Affiliation(s)
- Mao-Tong Liu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Ji-Yun Zhang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Lei Xu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Qi Qu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Meng-Tian Lu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Ji-Feng Jiang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Xian-Ce Zhao
- Philips Healthcare Shanghai, Shanghai, People's Republic of China
| | - Xue-Qin Zhang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
| | - Tao Zhang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
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Chang Y, Xing H, Shang Y, Liu Y, Yu L, Dai H. Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models. J Cancer Res Clin Oncol 2023; 149:15425-15438. [PMID: 37642725 DOI: 10.1007/s00432-023-05311-y] [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: 07/17/2023] [Accepted: 08/16/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE To construct and validate conventional and radiomics models based on dual-layer spectral CT radiomics for preoperative prediction of lung ground glass nodules (GGNs) invasiveness. MATERIALS AND METHODS A retrospective study was conducted on 176 GGNs patients who underwent chest non-contrast enhancement scan on dual-layer spectral detector CT at our hospital within 2 weeks before surgery. Patients were randomized into the training cohort and testing cohort. Clinical features, imaging features and spectral quantitative parameters were collected to establish a conventional model. Radiomics models were established by extracting 1781 radiomics features form regions of interest of each spectral image [120 kVp poly energetic images (PI), 60 keV images and electron density maps], respectively. After selecting the optimal radiomic features and integrating multiple machine learning models, the conventional model, PI model, 60 keV model, electron density (ED) model and combined model based on multimodal spectral images were finally established. The performance of these models was assessed through the evaluation of discrimination, calibration, and clinical application. RESULTS In the conventional model, age, vacuole sign, 60 keV and ED were independent risk factors of invasiveness. The combined model using logistic regression-least absolute shrinkage and selection operator classifiers was the optimal model with a higher area under the curve of the training (0.961, 95% confidence interval, CI: 0.932-0.991) and testing set (0.944, 0.890-0.999). CONCLUSION The combined models are helpful to predict the invasiveness of GGNs before surgery and guide the individualized treatment of patients.
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Affiliation(s)
- Yue Chang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Hanqi Xing
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Yi Shang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Yuanqing Liu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Lefan Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China
| | - Hui Dai
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China.
- Institute of Medical Imaging, Soochow University, Suzhou, 215006, Jiangsu Province, People's Republic of China.
- Suzhou Key Laboratory of Intelligent Medicine and Equipment, Suzhou, 215123, Jiangsu Province, People's Republic of China.
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Shaikh S. Editorial for "Preoperative Prediction of Dual-Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models". J Magn Reson Imaging 2023; 57:1197-1198. [PMID: 35986593 DOI: 10.1002/jmri.28390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 07/18/2022] [Indexed: 11/05/2022] Open
Affiliation(s)
- Sikandar Shaikh
- Department of Radiology, Shadan Institute of Medical Sciences, Hyderabad, India
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