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Wei G, Fang G, Guo P, Fang P, Wang T, Lin K, Liu J. Preoperative prediction of microvascular invasion risk in hepatocellular carcinoma with MRI: peritumoral versus tumor region. Insights Imaging 2024; 15:188. [PMID: 39090456 PMCID: PMC11294513 DOI: 10.1186/s13244-024-01760-2] [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: 04/06/2024] [Accepted: 06/23/2024] [Indexed: 08/04/2024] Open
Abstract
OBJECTIVES To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). METHODS A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). RESULTS The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. CONCLUSION Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. CLINICAL RELEVANCE STATEMENT The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. KEY POINTS We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI.
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Affiliation(s)
- Guangya Wei
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Guoxu Fang
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Pengfei Guo
- Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Peng Fang
- Department of Radiology, Henan Province Hospital of TCM, Zhengzhou, China
| | - Tongming Wang
- Department of Radiology, Henan Province Hospital of TCM, Zhengzhou, China
| | - Kecan Lin
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jingfeng Liu
- Department of Hepatopancreatobiliary Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China.
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Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med 2024; 173:108337. [PMID: 38547656 DOI: 10.1016/j.compbiomed.2024.108337] [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: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.
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Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiatao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ziyan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danyang Shu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Mottola M, Golfieri R, Bevilacqua A. The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging. SENSORS (BASEL, SWITZERLAND) 2024; 24:1156. [PMID: 38400314 PMCID: PMC10893370 DOI: 10.3390/s24041156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition's benefits, we compared the predictivity of models relying on both "standard" and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis.
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Affiliation(s)
- Margherita Mottola
- Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate), University of Bologna, 40126 Bologna, Italy;
| | - Rita Golfieri
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy;
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy
- Advanced Research Center on Electronic Systems (ARCES), University of Bologna, 40125 Bologna, Italy
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4
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Liu WM, Zhao XY, Gu MT, Song KR, Zheng W, Yu H, Chen HL, Xu XW, Zhou X, Liu AE, Jia NY, Wang PJ. Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma. World J Oncol 2024; 15:58-71. [PMID: 38274720 PMCID: PMC10807913 DOI: 10.14740/wjon1731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/15/2023] [Indexed: 01/27/2024] Open
Abstract
Background The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.
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Affiliation(s)
- Wan Min Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Xing Yu Zhao
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Meng Ting Gu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kai Rong Song
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Wei Zheng
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Yu
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Lin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiao Wen Xu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiang Zhou
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ai E Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Ning Yang Jia
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Pei Jun Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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Li J, Su X, Xu X, Zhao C, Liu A, Yang L, Song B, Song H, Li Z, Hao X. Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma. Crit Rev Oncol Hematol 2023; 190:104107. [PMID: 37633349 DOI: 10.1016/j.critrevonc.2023.104107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/22/2023] [Indexed: 08/28/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.
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Affiliation(s)
- Jian Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xin Su
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xiao Xu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Changchun Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Ang Liu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Liwen Yang
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Baoling Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Hao Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Zihan Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Xiangyong Hao
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China.
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Gaudiano C, Mottola M, Bianchi L, Corcioni B, Braccischi L, Tomassoni MT, Cattabriga A, Cocozza MA, Giunchi F, Schiavina R, Fanti S, Fiorentino M, Brunocilla E, Mosconi C, Bevilacqua A. An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience. Cancers (Basel) 2023; 15:3438. [PMID: 37444548 DOI: 10.3390/cancers15133438] [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: 05/22/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.
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Affiliation(s)
- Caterina Gaudiano
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Margherita Mottola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Lorenzo Bianchi
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Beniamino Corcioni
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lorenzo Braccischi
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | | | - Arrigo Cattabriga
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Riccardo Schiavina
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Stefano Fanti
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Department of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Michelangelo Fiorentino
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Eugenio Brunocilla
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy
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Li Z, Wang Y, Zhu Y, Xu J, Wei J, Xie J, Zhang J. Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma. Front Oncol 2023; 13:1195110. [PMID: 37434971 PMCID: PMC10331018 DOI: 10.3389/fonc.2023.1195110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/30/2023] [Indexed: 07/13/2023] Open
Abstract
Background and purpose The presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN. Materials and methods In this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation. Results Using the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods. Conclusion Our modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction.
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Affiliation(s)
- Zhi Li
- School of Medicine, Shanghai University, Shanghai, China
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Shanghai, China
| | - Yutao Wang
- The First Affiliated Hospital of Ningbo University, Ningbo, China
| | - Yuzhao Zhu
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Shanghai, China
| | - Jiafeng Xu
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Shanghai, China
| | - Jinzhu Wei
- School of Medicine, Shanghai University, Shanghai, China
| | - Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jian Zhang
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai University, Shanghai, China
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Skwirczyński M, Tabor Z, Lasek J, Schneider Z, Gibała S, Kucybała I, Urbanik A, Obuchowicz R. Deep Learning Algorithm for Differentiating Patients with a Healthy Liver from Patients with Liver Lesions Based on MR Images. Cancers (Basel) 2023; 15:3142. [PMID: 37370752 DOI: 10.3390/cancers15123142] [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: 04/26/2023] [Revised: 06/05/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The problems in diagnosing the state of a vital organ such as the liver are complex and remain unresolved. These problems are underscored by frequently published studies on this issue. At the same time, demand for imaging diagnostics, preferably using a method that can detect the disease at the earliest possible stage, is constantly increasing. In this paper, we present liver diseases in the context of diagnosis, diagnostic problems, and possible elimination. We discuss the dataset and methods and present the stages of the pipeline we developed, leading to multiclass segmentation of the liver in multiparametric MR image into lesions and normal tissue. Finally, based on the processing results, each case is classified as either a healthy liver or a liver with lesions. For the training set, the AUC ROC is 0.925 (standard error 0.013 and a p-value less than 0.001), and for the test set, the AUC ROC is 0.852 (standard error 0.039 and a p-value less than 0.001). Further refinements to the proposed pipeline are also discussed. The proposed approach could be used in the detection of focal lesions in the liver and the description of liver tumors. Practical application of the developed multi-class segmentation method represents a key step toward standardizing the medical evaluation of focal lesions in the liver.
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Affiliation(s)
- Maciej Skwirczyński
- Faculty of Mathematics and Computer Science, Jagiellonian University, 30-348 Krakow, Poland
| | - Zbisław Tabor
- Faculty of Electrical Engineering, Automatics, Computer Science, and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Julia Lasek
- Faculty of Geology, Geophysics, and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Zofia Schneider
- Faculty of Geology, Geophysics, and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | | | - Iwona Kucybała
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
| | - Andrzej Urbanik
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
| | - Rafał Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
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9
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Carlini G, Gaudiano C, Golfieri R, Curti N, Biondi R, Bianchi L, Schiavina R, Giunchi F, Faggioni L, Giampieri E, Merlotti A, Dall’Olio D, Sala C, Pandolfi S, Remondini D, Rustici A, Pastore LV, Scarpetti L, Bortolani B, Cercenelli L, Brunocilla E, Marcelli E, Coppola F, Castellani G. Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer. J Pers Med 2023; 13:jpm13030478. [PMID: 36983660 PMCID: PMC10052019 DOI: 10.3390/jpm13030478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/20/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023] Open
Abstract
Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.
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Affiliation(s)
- Gianluca Carlini
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Caterina Gaudiano
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
| | - Nico Curti
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
- Correspondence: (N.C.); (R.B.)
| | - Riccardo Biondi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
- Correspondence: (N.C.); (R.B.)
| | - Lorenzo Bianchi
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Riccardo Schiavina
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Roma, Italy
| | - Enrico Giampieri
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Alessandra Merlotti
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Daniele Dall’Olio
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Claudia Sala
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Sara Pandolfi
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy
- National Institute of Nuclear Physics, INFN, 40127 Bologna, Italy
| | - Arianna Rustici
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40138 Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
| | - Leonardo Scarpetti
- Dipartimento Diagnostica per Immagini AUSL Romagna, UOC Radiologia Faenza, 48018 Faenza, Italy
| | - Barbara Bortolani
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Laura Cercenelli
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Eugenio Brunocilla
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Emanuela Marcelli
- eDIMESLab, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy
- Dipartimento Diagnostica per Immagini AUSL Romagna, UOC Radiologia Faenza, 48018 Faenza, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 40138 Bologna, Italy
| | - Gastone Castellani
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
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10
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Radiomic Features from Post-Operative 18F-FDG PET/CT and CT Imaging Associated with Locally Recurrent Rectal Cancer: Preliminary Findings. J Clin Med 2023; 12:jcm12052058. [PMID: 36902845 PMCID: PMC10004457 DOI: 10.3390/jcm12052058] [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: 02/14/2023] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
Locally Recurrent Rectal Cancer (LRRC) remains a major clinical concern; it rapidly invades pelvic organs and nerve roots, causing severe symptoms. Curative-intent salvage therapy offers the only potential for cure but it has a higher chance of success when LRRC is diagnosed at an early stage. Imaging diagnosis of LRRC is very challenging due to fibrosis and inflammatory pelvic tissue, which can mislead even the most expert reader. This study exploited a radiomic analysis to enrich, through quantitative features, the characterization of tissue properties, thus favoring an accurate detection of LRRC by Computed Tomography (CT) and 18F-FDG-Positron Emission Tomography/CT (PET/CT). Of 563 eligible patients undergoing radical resection (R0) of primary RC, 57 patients with suspected LRRC were included, 33 of which were histologically confirmed. After manually segmenting suspected LRRC in CT and PET/CT, 144 Radiomic Features (RFs) were generated, and RFs were investigated for univariate significant discriminations (Wilcoxon rank-sum test, p < 0.050) of LRRC from NO LRRC. Five RFs in PET/CT (p < 0.017) and two in CT (p < 0.022) enabled, individually, a clear distinction of the groups, and one RF was shared by PET/CT and CT. As well as confirming the potential role of radiomics to advance LRRC diagnosis, the aforementioned shared RF describes LRRC as tissues having high local inhomogeneity due to the evolving tissue's properties.
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11
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Polici M, Laghi A, Caruso D. Editorial for Special Issue on Imaging Biomarker in Oncology. Cancers (Basel) 2023; 15:cancers15041071. [PMID: 36831414 PMCID: PMC9954371 DOI: 10.3390/cancers15041071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023] Open
Abstract
Imaging biomarkers are the expression of quantitative imaging and have become central in the management of cancers, proving consistent and objective information to outline an appropriate workflow for oncologic patients [...].
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12
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Wei J, Jiang H, Zhou Y, Tian J, Furtado FS, Catalano OA. Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma. Dig Liver Dis 2023:S1590-8658(22)00863-5. [PMID: 36641292 DOI: 10.1016/j.dld.2022.12.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/16/2023]
Abstract
The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China.
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR. China
| | - Yu Zhou
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; School of Life Science and Technology, Xidian University, Xi'an, PR. China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, PR. China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR. China.
| | - Felipe S Furtado
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.
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13
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Jiang Y, Wang K, Wang YR, Xiang YJ, Liu ZH, Feng JK, Cheng SQ. Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence. Technol Cancer Res Treat 2023; 22:15330338231212726. [PMID: 37933176 PMCID: PMC10631353 DOI: 10.1177/15330338231212726] [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: 07/26/2023] [Revised: 10/01/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.
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Affiliation(s)
- Yu Jiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Kang Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Yu-Ran Wang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yan-Jun Xiang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Zong-Han Liu
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jin-Kai Feng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Shu-Qun Cheng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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14
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Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions. Cancers (Basel) 2022; 14:cancers14246156. [PMID: 36551642 PMCID: PMC9776977 DOI: 10.3390/cancers14246156] [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: 11/08/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.
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15
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Yang X, Shao G, Liu J, Liu B, Cai C, Zeng D, Li H. Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system. Front Oncol 2022; 12:1021570. [DOI: 10.3389/fonc.2022.1021570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022] Open
Abstract
PurposesThis study aimed to establish a predictive model of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) by contrast-enhanced computed tomography (CT), which relied on a combination of machine learning approach and imaging features covering Liver Imaging and Reporting and Data System (LI-RADS) features.MethodsThe retrospective study included 279 patients with surgery who underwent preoperative enhanced CT. They were randomly allocated to training set, validation set, and test set (167 patients vs. 56 patients vs. 56 patients, respectively). Significant imaging findings for predicting MVI were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression method. Predictive models were performed by machine learning algorithm, support vector machine (SVM), in the training set and validation set, and evaluated in the test set. Further, a combined model adding clinical findings to the radiologic model was developed. Based on the LI-RADS category, subgroup analyses were conducted.ResultsWe included 116 patients with MVI which were diagnosed through pathological confirmation. Six imaging features were selected about MVI prediction: four LI-RADS features (corona enhancement, enhancing capsule, non-rim aterial phase hyperehancement, tumor size) and two non-LI-RADS features (internal arteries, non-smooth tumor margin). The radiological feature with the best accuracy was corona enhancement followed by internal arteries and tumor size. The accuracies of the radiological model and combined model were 0.725–0.714 and 0.802–0.732 in the training set, validation set, and test set, respectively. In the LR-4/5 subgroup, a sensitivity of 100% and an NPV of 100% were obtained by the high-sensitivity threshold. A specificity of 100% and a PPV of 100% were acquired through the high specificity threshold in the LR-M subgroup.ConclusionA combination of LI-RADS features and non-LI-RADS features and serum alpha-fetoprotein value could be applied as a preoperative biomarker for predicting MVI by the machine learning approach. Furthermore, its good performance in the subgroup by LI-RADS category may help optimize the management of HCC patients.
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16
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Wang F, Cheng M, Du B, Li LM, Huang WP, Gao JB. Use of radiomics containing an effective peritumoral area to predict early recurrence of solitary hepatocellular carcinoma ≤5 cm in diameter. Front Oncol 2022; 12:1032115. [PMID: 36387096 PMCID: PMC9650218 DOI: 10.3389/fonc.2022.1032115] [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: 08/30/2022] [Accepted: 10/07/2022] [Indexed: 01/27/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is the sixth leading type of cancer worldwide. We aimed to develop a preoperative predictive model of the risk of early tumor recurrence after HCC treatment based on radiomic features of the peritumoral region and evaluate the performance of this model against postoperative pathology. Method Our model was developed using a retrospective analysis of imaging and clinicopathological data of 175 patients with an isolated HCC ≤5 cm in diameter; 117 patients were used for model training and 58 for model validation. The peritumoral area was delineated layer-by-layer for the arterial and portal vein phase on preoperative dynamic enhanced computed tomography images. The volume area of interest was expanded by 5 and 10 mm and the radiomic features of these areas extracted. Lasso was used to select the most stable features. Results The radiomic features of the 5-mm area were sufficient for prediction of early tumor recurrence, with an area under the curve (AUC) value of 0.706 for the validation set and 0.837 for the training set using combined images. The AUC of the model using clinicopathological information alone was 0.753 compared with 0.786 for the preoperative radiomics model (P >0.05). Conclusions Radiomic features of a 5-mm peritumoral region may provide a non-invasive biomarker for the preoperative prediction of the risk of early tumor recurrence for patients with a solitary HCC ≤5 cm in diameter. A fusion model that combines the radiomic features of the peritumoral region and postoperative pathology could contribute to individualized treatment of HCC.
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Affiliation(s)
- Fang Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Cheng
- Information Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Binbin Du
- Vasculocardiology Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-ming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wen-peng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Jian-bo Gao,
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17
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Yuan X, Zhuang M, Zhu X, Cheng D, Liu J, Sun D, Qiu X, Lu Y, Sartorius K. Emerging Perspectives of Bone Metastasis in Hepatocellular Carcinoma. Front Oncol 2022; 12:943866. [PMID: 35847843 PMCID: PMC9277479 DOI: 10.3389/fonc.2022.943866] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 05/30/2022] [Indexed: 12/09/2022] Open
Abstract
Recent evidence suggests the global incidence and mortality of hepatocellular carcinoma (HCC) are increasing. Although the highest incidence of HCC remains entrenched in WHO regions with high levels of HBV-HCV infection, the etiology of this disease is rapidly changing to include other lifestyle risk factors. Extrahepatic metastasis is a frequent feature of advanced HCC and most commonly locates in the lungs and bone. Bone metastasis in HCC (HCC-BM) signals a more aggressive stage of disease and a poorer prognosis, simultaneously HCC-BM compromises the function and integrity of bone tissue. HCC induced osteolysis is a prominent feature of metastasis that complicates treatment needed for pathologic fractures, bone pain and other skeletal events like hypercalcemia and nerve compression. Early detection of bone metastases facilitates the treatment strategy for avoiding and relieving complications. Although recent therapeutic advances in HCC like targeting agents and immunotherapy have improved survival, the prognosis for patients with HCC-BM remains problematic. The identification of critical HCC-BM pathways in the bone microenvironment could provide important insights to guide future detection and therapy. This review presents an overview of the clinical development of bone metastases in HCC, identifying key clinical features and identifying potential molecular targets that can be deployed as diagnostic tools or therapeutic agents.
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Affiliation(s)
- Xiaofeng Yuan
- The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Ming Zhuang
- The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Xi Zhu
- The Third Affiliated Hospital of Soochow University, Changzhou, China
- Department of Infectious Diseases, The First Peoples’ Hospital of Kunshan, Kunshan, China
| | - Dong Cheng
- The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Jie Liu
- The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Donglin Sun
- The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Xubin Qiu
- The Third Affiliated Hospital of Soochow University, Changzhou, China
- *Correspondence: Xubin Qiu, ; Yunjie Lu, ; Kurt Sartorius,
| | - Yunjie Lu
- The Third Affiliated Hospital of Soochow University, Changzhou, China
- *Correspondence: Xubin Qiu, ; Yunjie Lu, ; Kurt Sartorius,
| | - Kurt Sartorius
- Hepatitis Diversity Research Unit, School of Internal Medicine, University of the Witwatersrand, Johannesburg, South Africa
- Africa Hepatopancreatobiliary Cancer Consortium (AHPBCC), Mayo Clinic, Jacksonville, FL, United States
- School of Laboratory Medicine and Molecular Sciences, College of Health Science, University of KwaZulu-Natal, Durban, South Africa
- *Correspondence: Xubin Qiu, ; Yunjie Lu, ; Kurt Sartorius,
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