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Han Y, Chang Y, Wang J, Li N, Yu Y, Yang Z, Lv W, Liu W, Yin J, Wu J. A study predicting long-term survival capacity in postoperative advanced gastric cancer patients based on MAOA and subcutaneous muscle fat characteristics. World J Surg Oncol 2024; 22:184. [PMID: 39010072 PMCID: PMC11251287 DOI: 10.1186/s12957-024-03466-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND The prognosis of advanced gastric cancer (AGC) is relatively poor, and long-term survival depends on timely intervention. Currently, predicting survival rates remains a hot topic. The application of radiomics and immunohistochemistry-related techniques in cancer research is increasingly widespread. However, their integration for predicting long-term survival in AGC patients has not been fully explored. METHODS We Collected 150 patients diagnosed with AGC at the Affiliated Zhongshan Hospital of Dalian University who underwent radical surgery between 2015 and 2019. Following strict inclusion and exclusion criteria, 90 patients were included in the analysis. We Collected postoperative pathological specimens from enrolled patients, analyzed the expression levels of MAOA using immunohistochemical techniques, and quantified these levels as the MAOAHScore. Obtained plain abdominal CT images from patients, delineated the region of interest at the L3 vertebral body level, and extracted radiomics features. Lasso Cox regression was used to select significant features to establish a radionics risk score, convert it into a categorical variable named risk, and use Cox regression to identify independent predictive factors for constructing a clinical prediction model. ROC, DCA, and calibration curves validated the model's performance. RESULTS The enrolled patients had an average age of 65.71 years, including 70 males and 20 females. Multivariate Cox regression analysis revealed that risk (P = 0.001, HR = 3.303), MAOAHScore (P = 0.043, HR = 2.055), and TNM stage (P = 0.047, HR = 2.273) emerged as independent prognostic risk factors for 3-year overall survival (OS) and The Similar results were found in the analysis of 3-year disease-specific survival (DSS). The nomogram developed could predict 3-year OS and DSS rates, with areas under the ROC curve (AUCs) of 0.81 and 0.797, respectively. Joint calibration and decision curve analyses (DCA) confirmed the nomogram's good predictive performance and clinical utility. CONCLUSION Integrating immunohistochemistry and muscle fat features provides a more accurate prediction of long-term survival in gastric cancer patients. This study offers new perspectives and methods for a deeper understanding of survival prediction in AGC.
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Grants
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
- No. 243, 2021 Dalian Deng Feng Program
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Affiliation(s)
- Yubo Han
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Yaoyuan Chang
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jiaqi Wang
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Nanbo Li
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Yang Yu
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Zhengbo Yang
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Weipeng Lv
- Department of Pathology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Wenfei Liu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jiajun Yin
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
| | - Ju Wu
- Department of General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
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Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka SP, George B, Furman F, Khan N, Plumptre A, Sekhon I, Lo A, Weissman S. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024; 5:90704. [DOI: 10.37126/aige.v5.i2.90704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/28/2024] [Accepted: 03/04/2024] [Indexed: 05/11/2024] Open
Abstract
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
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Affiliation(s)
- Ayrton Bangolo
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nikita Wadhwani
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Vignesh K Nagesh
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Shraboni Dey
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Hadrian Hoang-Vu Tran
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Izage Kianifar Aguilar
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Auda Auda
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aman Sidiqui
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aiswarya Menon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Deborah Daoud
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - James Liu
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Sai Priyanka Pulipaka
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Blessy George
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Flor Furman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nareeman Khan
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Adewale Plumptre
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Imranjot Sekhon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Abraham Lo
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Simcha Weissman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
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Yoon S, Kim YJ, Jeon JS, Ahn SJ, Choi SJ. Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases. KOREAN JOURNAL OF CLINICAL ONCOLOGY 2024; 20:27-35. [PMID: 38988016 PMCID: PMC11261177 DOI: 10.14216/kjco.24005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 05/31/2024] [Accepted: 06/17/2024] [Indexed: 07/12/2024]
Abstract
PURPOSE The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy. METHODS We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses. RESULTS Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605-0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8-22.2), and 14.8 months (95% CI, 14.2-15.4), respectively (P = 0.431, log-rank test). CONCLUSION Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.
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Affiliation(s)
- Sungjin Yoon
- Department of Radiology, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon,
Korea
| | - Young Jae Kim
- Biomedical Engineering, Gachon University College of Medicine, Incheon,
Korea
| | - Ji Soo Jeon
- Biomedical Engineering, Gachon University College of Medicine, Incheon,
Korea
| | - Su Joa Ahn
- Department of Radiology, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon,
Korea
| | - Seung Joon Choi
- Department of Radiology, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon,
Korea
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Mou M, Gao R, Wu Y, Lin P, Yin H, Chen F, Huang F, Wen R, Yang H, He Y. Endoscopic Rectal Ultrasound-Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:361-373. [PMID: 37950599 DOI: 10.1002/jum.16369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 09/26/2023] [Accepted: 10/22/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVES To develop and validate an ultrasound-based radiomics model to predict synchronous liver metastases (SLM) in rectal cancer (RC) patients preoperatively. METHODS Two hundred and thirty-nine RC patients were included in this study and randomly divided into training and validation cohorts. A total of 5936 radiomics features were calculated on the basis of ultrasound images to build a radiomic model and obtain a radiomics score (Rad-score) using logistic regression. Meanwhile, clinical characteristics were collected to construct a clinical model. The radiomics-clinical model was developed and validated by integrating the radiomics features with the selected clinical characteristics. The performances of three models were evaluated and compared through their discrimination, calibration, and clinical usefulness. RESULTS The radiomics model was developed based on 13 radiomic features. The radiomics-clinical model, which incorporated Rad-score, CEA, and CA199, exhibited favorable discrimination and calibration with areas under the receiver operating characteristic curve (AUC) of 0.920 (95% CI: 0.874-0.965) in the training cohorts and 0.855 (95% CI: 0.759-0.951) in the validation cohorts. And the AUC of the radiomics-clinical model was 0.849 (95% CI: 0.771-0.927) for the training cohorts and 0.780 (95% CI: 0.655-0.905) for the validation cohorts, the clinical model was 0.811 (95% CI: 0.718-0.905) for the training cohorts and 0.805 (95% CI: 0.645-0.965) for the validation cohorts. Moreover, decision curve analysis (DCA) further confirmed the clinical utility of the radiomics-clinical model. CONCLUSIONS The radiomics-clinical model performed satisfactory predictive performance, which can help improve clinical diagnosis performance and outcome prediction for SLM in RC patients.
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Affiliation(s)
- Meiyan Mou
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Medical Ultrasound, Yulin No. 1 People's Hospital of Guangxi Zhuang Autonomous Region, Yulin, China
| | - Ruizhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuquan Wu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hongxia Yin
- Department of Medical Ultrasound, Yulin No. 1 People's Hospital of Guangxi Zhuang Autonomous Region, Yulin, China
| | - Fenghuan Chen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Fen Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Choi BS, Yoo SK, Moon J, Chung SY, Oh J, Baek S, Kim Y, Chang JS, Kim H, Kim JS. Acute coronary event (ACE) prediction following breast radiotherapy by features extracted from 3D CT, dose, and cardiac structures. Med Phys 2023; 50:6409-6420. [PMID: 36974390 DOI: 10.1002/mp.16398] [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: 06/14/2022] [Revised: 02/22/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023] Open
Abstract
PURPOSE Heart toxicity, such as major acute coronary events (ACE), following breast radiation therapy (RT) is of utmost concern. Thus, many studies have been investigating the effect of mean heart dose (MHD) and dose received in heart sub-structures on toxicity. Most studies focused on the dose thresholds in the heart and its sub-structures, while few studies adopted such computational methods as deep neural networks (DNN) and radiomics. This work aims to construct a feature-driven predictive model for ACE after breast RT. METHODS A recently proposed two-step predictive model that extracts a number of features from a deep auto-segmentation network and processes the selected features for prediction was adopted. This work refined the auto-segmenting network and feature processing algorithms to enhance performance in cardiac toxicity prediction. In the predictive model, the deep convolutional neural network (CNN) extracted features from 3D computed tomography (CT) images and dose distributions in three automatically segmented heart sub-structures, including the left anterior descending artery (LAD), right coronary artery (RCA), and left ventricle (LV). The optimal feature processing workflow for the extracted features was explored to enhance the prediction accuracy. The regions associated with toxicity were visualized using a class activation map (CAM)-based technique. Our proposed model was validated against a conventional DNN (convolutional and fully connected layers) and radiomics with a patient cohort of 84 cases, including 29 and 55 patient cases with and without ACE. Of the entire 84 cases, 12 randomly chosen cases (5 toxicity and 7 non-toxicity cases) were set aside for independent test, and the remaining 72 cases were applied to 4-fold stratified cross-validation. RESULTS Our predictive model outperformed the conventional DNN by 38% and 10% and radiomics-based predictive models by 9% and 10% in AUC for 4-fold cross-validations and independent test, respectively. The degree of enhancement was greater when incorporating dose information and heart sub-structures into feature extraction. The model whose inputs were CT, dose, and three sub-structures (LV, LAD, and RCA) reached 96% prediction accuracy on average and 0.94 area under the curve (AUC) on average in the cross-validation, and also achieved prediction accuracy of 83% and AUC of 0.83 in the independent test. On 10 correctly predicted cases out of 12 for the independent test, the activation maps implied that for cases of ACE toxicity, the higher intensity was more likely to be observed inside the LV. CONCLUSIONS The proposed model characterized by modifications in model input with dose distributions and cardiac sub-structures, and serial processing of feature extraction and feature selection techniques can improve the predictive performance in ACE following breast RT.
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Affiliation(s)
- Byong Su Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Sang Kyun Yoo
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jinyoung Moon
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Yeun Chung
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, South Korea
| | - Jaewon Oh
- Cardiology Division, Severance Cardiovascular Hospital, and Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Stephen Baek
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - Yusung Kim
- Department of Radiation Physics, The Universiy of Texas MD Anderson Cancer Center, Texas, USA
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Gangnam Severance Hospital, Seoul, South Korea
| | - Hojin Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
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Sun C, Liu X, Sun J, Dong L, Wei F, Bao C, Zhong J, Li Y. A CT-based radiomics nomogram for predicting histopathologic growth patterns of colorectal liver metastases. J Cancer Res Clin Oncol 2023; 149:9543-9555. [PMID: 37221440 DOI: 10.1007/s00432-023-04852-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 05/11/2023] [Indexed: 05/25/2023]
Abstract
PURPOSE To develop a computed tomography (CT)-based radiomics nomogram for pre-treatment prediction of histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLM) and to validate its accuracy and clinical value. MATERIALS AND METHODS This retrospective study included a total of 197 CRLM from 92 patients. Lesions from CRLM were randomly divided into the training study (n = 137) and the validation study (n = 60) with the ratio of 3:1 for model construction and internal validation. The least absolute shrinkage and selection operator (LASSO) was used to screen features. Radiomics score (rad-score) was calculated to generate radiomics features. A predictive radiomics nomogram based on rad-score and clinical features was developed using random forest (RF). The performances of clinical model, radiomic model and radiomics nomogram were thoroughly evaluated by the DeLong test, decision curve analysis (DCA) and clinical impact curve (CIC) allowing for generation of an optimal predictive model. RESULTS The radiological nomogram model consists of three independent predictors, including rad-score, T-stage, and enhancement rim on PVP. Training and validation results demonstrated the high-performance level of the model of area under curve (AUC) of 0.86 and 0.84, respectively. The radiomic nomogram model can achieve better diagnostic performance than the clinical model, yielding greater net clinical benefit compared to the clinical model alone. CONCLUSIONS A CT-based radiomics nomogram can be used to predict HGPs in CRLM. Preoperative non-invasive identification of HGPs could further facilitate clinical treatment and provide personalized treatment plans for patients with liver metastases from colorectal cancer.
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Affiliation(s)
- Chao Sun
- Department of Radiology, Tianjin Union Medical Center, Jieyuan Road, Hongqiao District, Tianjin, 300121, People's Republic of China
| | - Xuehuan Liu
- Department of Radiology, Tianjin Union Medical Center, Jieyuan Road, Hongqiao District, Tianjin, 300121, People's Republic of China
| | - Jie Sun
- Department of Pathology, Tianjin Union Medical Center, Tianjin, 300121, People's Republic of China
| | - Longchun Dong
- Department of Radiology, Tianjin Union Medical Center, Jieyuan Road, Hongqiao District, Tianjin, 300121, People's Republic of China
| | - Feng Wei
- Department of Radiology, Tianjin Union Medical Center, Jieyuan Road, Hongqiao District, Tianjin, 300121, People's Republic of China
| | - Cuiping Bao
- Department of Radiology, Tianjin Union Medical Center, Jieyuan Road, Hongqiao District, Tianjin, 300121, People's Republic of China
| | - Jin Zhong
- Department of Radiology, Tianjin Union Medical Center, Jieyuan Road, Hongqiao District, Tianjin, 300121, People's Republic of China
| | - Yiming Li
- Department of Radiology, Tianjin Union Medical Center, Jieyuan Road, Hongqiao District, Tianjin, 300121, People's Republic of China.
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Lakshmipriya B, Pottakkat B, Ramkumar G. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review. Artif Intell Med 2023; 141:102557. [PMID: 37295904 DOI: 10.1016/j.artmed.2023.102557] [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: 04/04/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.
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Affiliation(s)
- B Lakshmipriya
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Biju Pottakkat
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
| | - G Ramkumar
- Department of Radio Diagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
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Franco D, Granata V, Fusco R, Grassi R, Nardone V, Lombardi L, Cappabianca S, Conforti R, Briganti F, Grassi R, Caranci F. Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01655-0. [PMID: 37289266 DOI: 10.1007/s11547-023-01655-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023]
Abstract
PURPOSE The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM). METHODS GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features. RESULTS A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT. CONCLUSIONS AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.
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Affiliation(s)
- Donatella Franco
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Research & Development and Medical Oncology Division, Igea SpA, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Valerio Nardone
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Laura Lombardi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Salvatore Cappabianca
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Renata Conforti
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Francesco Briganti
- Advanced Biomedical Sciences Department, Federico II University, Naples, Italy
| | - Roberto Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Ferdinando Caranci
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
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10
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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11
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023; 18:18. [PMID: 36927442 PMCID: PMC10018963 DOI: 10.1186/s13027-023-00495-x] [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: 01/31/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Napoli, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, Milan, 20122, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", Bari, 70124, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Guglielmo Nasti
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Lucrezia Silvestro
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Antonio Cassata
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, 80138, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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12
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Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023; 189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
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Affiliation(s)
- Anita Bakrania
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | | | - Xun Zhao
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Medical Sciences, Toronto, ON, Canada.
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13
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Petinrin OO, Saeed F, Toseef M, Liu Z, Basurra S, Muyide IO, Li X, Lin Q, Wong KC. Machine Learning in Metastatic Cancer Research: Potentials, Possibilities, and Prospects. Comput Struct Biotechnol J 2023; 21:2454-2470. [PMID: 37077177 PMCID: PMC10106342 DOI: 10.1016/j.csbj.2023.03.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner.
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14
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Qu H, Zhai H, Zhang S, Chen W, Zhong H, Cui X. Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases. Front Oncol 2023; 13:992096. [PMID: 36814812 PMCID: PMC9939899 DOI: 10.3389/fonc.2023.992096] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/12/2023] [Indexed: 02/08/2023] Open
Abstract
Background and objective For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions. Methods In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS). Results For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis). Conclusions This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.
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Affiliation(s)
- Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, P.R, China
| | - Huan Zhai
- Department of Interventional Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China,Key Laboratory of Diagnostic Imaging and Interventional Radiology of Liaoning Province, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Shuairan Zhang
- Department of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Wenjuan Chen
- Department of Medical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Hongshan Zhong
- Department of Interventional Radiology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China,Key Laboratory of Diagnostic Imaging and Interventional Radiology of Liaoning Province, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China,*Correspondence: Xiaoyu Cui, ; Hongshan Zhong,
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, P.R, China,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China,*Correspondence: Xiaoyu Cui, ; Hongshan Zhong,
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15
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Granata V, Fusco R, De Muzio F, Cutolo C, Grassi F, Brunese MC, Simonetti I, Catalano O, Gabelloni M, Pradella S, Danti G, Flammia F, Borgheresi A, Agostini A, Bruno F, Palumbo P, Ottaiano A, Izzo F, Giovagnoni A, Barile A, Gandolfo N, Miele V. Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence. BIOLOGY 2023; 12:biology12020213. [PMID: 36829492 PMCID: PMC9952965 DOI: 10.3390/biology12020213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Salerno, Italy
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Orlando Catalano
- Radiology Unit, Istituto Diagnostico Varelli, Via Cornelia dei Gracchi 65, 80126 Naples, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56216 Pisa, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Federica Flammia
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federico Bruno
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Pierpaolo Palumbo
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Alessandro Ottaiano
- SSD Innovative Therapies for Abdominal Metastases, Istituto Nazionale Tumori IRCCS-Fondazione G. Pascale, 80130 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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Caruso M, Stanzione A, Prinster A, Pizzuti LM, Brunetti A, Maurea S, Mainenti PP. Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases. World J Gastroenterol 2023; 29:521-535. [PMID: 36688023 PMCID: PMC9850941 DOI: 10.3748/wjg.v29.i3.521] [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: 10/06/2022] [Revised: 11/25/2022] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
In patients with colorectal liver metastasis (CRLMs) unsuitable for surgery, oncological treatments, such as chemotherapy and targeted agents, can be performed. Cross-sectional imaging [computed tomography (CT), magnetic resonance imaging (MRI), 18-fluorodexoyglucose positron emission tomography with CT/MRI] evaluates the response of CRLMs to therapy, using post-treatment lesion shrinkage as a qualitative imaging parameter. This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them. Consequently, there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients. Advanced quantitative imaging (diffusion-weighted imaging, perfusion imaging, molecular imaging) allows the in vivo evaluation of specific biological tissue features described as quantitative parameters. Furthermore, radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters. As a result, parametric analysis (PA) translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion, structural heterogeneity, cellularity, oxygenation, and glucose consumption. PA could be a potentially useful imaging marker for predicting CRLMs treatment response. This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.
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Affiliation(s)
- Martina Caruso
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Anna Prinster
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
| | - Laura Micol Pizzuti
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Research Council, Napoli 80131, Italy
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Cutolo C, Fusco R, Simonetti I, De Muzio F, Grassi F, Trovato P, Palumbo P, Bruno F, Maggialetti N, Borgheresi A, Bruno A, Chiti G, Bicci E, Brunese MC, Giovagnoni A, Miele V, Barile A, Izzo F, Granata V. Imaging Features of Main Hepatic Resections: The Radiologist Challenging. J Pers Med 2023; 13:jpm13010134. [PMID: 36675795 PMCID: PMC9862253 DOI: 10.3390/jpm13010134] [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: 11/19/2022] [Revised: 12/31/2022] [Accepted: 01/06/2023] [Indexed: 01/12/2023] Open
Abstract
Liver resection is still the most effective treatment of primary liver malignancies, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), and of metastatic disease, such as colorectal liver metastases. The type of liver resection (anatomic versus non anatomic resection) depends on different features, mainly on the type of malignancy (primary liver neoplasm versus metastatic lesion), size of tumor, its relation with blood and biliary vessels, and the volume of future liver remnant (FLT). Imaging plays a critical role in postoperative assessment, offering the possibility to recognize normal postoperative findings and potential complications. Ultrasonography (US) is the first-line diagnostic tool to use in post-surgical phase. However, computed tomography (CT), due to its comprehensive assessment, allows for a more accurate evaluation and more normal findings than the possible postoperative complications. Magnetic resonance imaging (MRI) with cholangiopancreatography (MRCP) and/or hepatospecific contrast agents remains the best tool for bile duct injuries diagnosis and for ischemic cholangitis evaluation. Consequently, radiologists should be familiar with the surgical approaches for a better comprehension of normal postoperative findings and of postoperative complications.
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Affiliation(s)
- Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
- Correspondence:
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Piero Trovato
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
| | - Alessandra Bruno
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
| | - Giuditta Chiti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Eleonora Bicci
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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18
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Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence. Cancers (Basel) 2023; 15:cancers15020351. [PMID: 36672301 PMCID: PMC9857317 DOI: 10.3390/cancers15020351] [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] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Chenhui Yao,
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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20
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Su X, Zhang H, Wang Y. A predictive model for early therapeutic efficacy of colorectal liver metastases using multimodal MRI data. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:357-372. [PMID: 36591694 DOI: 10.3233/xst-221317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BACKGROUND Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome.
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Affiliation(s)
- Xuan Su
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yuanjun Wang
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
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21
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Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28:6363-6379. [PMID: 36533112 PMCID: PMC9753055 DOI: 10.3748/wjg.v28.i45.6363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/25/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Hao-Ming Yan
- School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Zhong-Ren Wang
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Hon Ho Yu
- Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
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22
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Ye Z, Xuan R, Ouyang M, Wang Y, Xu J, Jin W. Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study. Abdom Radiol (NY) 2022; 47:4205-4218. [PMID: 36094660 DOI: 10.1007/s00261-022-03673-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 06/15/2023]
Abstract
PURPOSE To achieve prenatal prediction of placenta accreta spectrum (PAS) by combining clinical model, radiomics model, and deep learning model using T2-weighted images (T2WI), and to objectively evaluate the performance of the prediction through multicenter validation. METHODS A total of 407 pregnant women from two centers undergoing preoperative magnetic resonance imaging (MRI) were retrospectively recruited. The patients from institution I were divided into a training cohort (n = 298) and a validation cohort (n = 75), while patients from institution II served as the external test cohort (n = 34). In this study, we built a clinical prediction model using patient clinical data, a radiomics model based on selected key features, and a deep learning model by mining deep semantic features. Based on this, we developed a combined model by ensembling the prediction results of the three models mentioned above to achieve prenatal prediction of PAS. The performance of these predictive models was evaluated with respect to discrimination, calibration, and clinical usefulness. RESULTS The combined model achieved AUCs of 0.872 (95% confidence interval, 0.843 to 0.908) in the validation cohort and 0.857 (0.808 to 0.894) in the external test cohort, both of which outperformed the other models. The calibration curves demonstrated excellent consistency in the validation cohort and the external test cohort, and the decision curves indicated high clinical usefulness. CONCLUSION By using preoperative clinical information and MRI images, the combined model can accurately predict PAS by ensembling clinical model, radiomics model, and deep learning model.
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Affiliation(s)
- Zhengjie Ye
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China
| | - Rongrong Xuan
- Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China
| | - Menglin Ouyang
- Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China
| | - Yutao Wang
- Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China
| | - Jian Xu
- Ningbo Women's and Children's Hospital, Ningbo, 315012, China
| | - Wei Jin
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
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23
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Miao S, Jia H, Cheng K, Hu X, Li J, Huang W, Wang R. Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients. Brief Bioinform 2022; 23:6748489. [PMID: 36198668 DOI: 10.1093/bib/bbac432] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 12/14/2022] Open
Abstract
Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.
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Affiliation(s)
- Shidi Miao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Haobo Jia
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Ke Cheng
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaohui Hu
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Jing Li
- Department of Geriatrics, the Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Wenjuan Huang
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
| | - Ruitao Wang
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China
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24
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A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques. Healthcare (Basel) 2022; 10:healthcare10102075. [DOI: 10.3390/healthcare10102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022] Open
Abstract
An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.
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25
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Hewitt DB, Brown ZJ, Pawlik TM. The Role of Biomarkers in the Management of Colorectal Liver Metastases. Cancers (Basel) 2022; 14:cancers14194602. [PMID: 36230522 PMCID: PMC9559307 DOI: 10.3390/cancers14194602] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 09/17/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Colorectal cancer remains one of the most significant sources of cancer-related morbidity and mortality worldwide. The liver is the most common site of metastatic spread. Multiple modalities exist to manage and potentially cure patients with metastatic colorectal cancer. However, reliable biomarkers to assist with clinical decision-making are limited. Recent advances in genomic sequencing technology have greatly expanded our knowledge of colorectal cancer carcinogenesis and significantly reduced the cost and timing of the investigation. In this article, we discuss the current utility of biomarkers in the management of colorectal cancer liver metastases. Abstract Surgical management combined with improved systemic therapies have extended 5-year overall survival beyond 50% among patients with colorectal liver metastases (CRLM). Furthermore, a multitude of liver-directed therapies has improved local disease control for patients with unresectable CRLM. Unfortunately, a significant portion of patients treated with curative-intent hepatectomy develops disease recurrence. Traditional markers fail to risk-stratify and prognosticate patients with CRLM appropriately. Over the last few decades, advances in molecular sequencing technology have greatly expanded our knowledge of the pathophysiology and tumor microenvironment characteristics of CRLM. These investigations have revealed biomarkers with the potential to better inform management decisions in patients with CRLM. Actionable biomarkers such as RAS and BRAF mutations, microsatellite instability/mismatch repair status, and tumor mutational burden have been incorporated into national and societal guidelines. Other biomarkers, including circulating tumor DNA and radiomic features, are under active investigation to evaluate their clinical utility. Given the plethora of therapeutic modalities and lack of evidence on timing and sequence, reliable biomarkers are needed to assist clinicians with the development of patient-tailored management plans. In this review, we discuss the current evidence regarding biomarkers for patients with CRLM.
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26
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Horvat N, Miranda J, El Homsi M, Peoples JJ, Long NM, Simpson AL, Do RKG. A primer on texture analysis in abdominal radiology. Abdom Radiol (NY) 2022; 47:2972-2985. [PMID: 34825946 DOI: 10.1007/s00261-021-03359-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 01/18/2023]
Abstract
The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.
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Affiliation(s)
- Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Niamh M Long
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
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27
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Russo V, Lallo E, Munnia A, Spedicato M, Messerini L, D’Aurizio R, Ceroni EG, Brunelli G, Galvano A, Russo A, Landini I, Nobili S, Ceppi M, Bruzzone M, Cianchi F, Staderini F, Roselli M, Riondino S, Ferroni P, Guadagni F, Mini E, Peluso M. Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:4012. [PMID: 36011003 PMCID: PMC9406544 DOI: 10.3390/cancers14164012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/26/2022] [Accepted: 08/12/2022] [Indexed: 12/24/2022] Open
Abstract
Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.
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Affiliation(s)
- Valentina Russo
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Eleonora Lallo
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Armelle Munnia
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Miriana Spedicato
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Luca Messerini
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Romina D’Aurizio
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Elia Giuseppe Ceroni
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Giulia Brunelli
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Antonio Galvano
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Antonio Russo
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Ida Landini
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Stefania Nobili
- Department of Neurosciences, Imaging and Clinical Sciences, “G. D’Annunzio” Chieti-Pescara, 66100 Chieti, Italy
| | - Marcello Ceppi
- Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy
| | - Marco Bruzzone
- Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy
| | - Fabio Cianchi
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Fabio Staderini
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Mario Roselli
- Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Silvia Riondino
- Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Patrizia Ferroni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
| | - Fiorella Guadagni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
| | - Enrico Mini
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Marco Peluso
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
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28
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Zheng Y, Wang H, Li Q, Sun H, Guo L. Discriminating Between Benign and Malignant Solid Ovarian Tumors Based on Clinical and Radiomic Features of MRI. Acad Radiol 2022; 30:814-822. [PMID: 35810066 DOI: 10.1016/j.acra.2022.06.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/31/2022] [Accepted: 06/08/2022] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a combined model integrating clinical and radiomic features to non-invasive discriminate between the benign and malignant solid ovarian tumors. MATERIALS AND METHODS A total of 148 patients with 156 solid ovarian tumors (86 benign and 70 malignant tumors) were included in this study. The dataset was split into the training and the test set with a ratio of 8:2 using stratified random sampling. 12 clinical features and 1612 radiomic features were extracted from each tumor. These features were selected by least absolute shrinkage and selection operator (Lasso). Three classification models were built using extreme gradient boosting (XGB) algorithm: clinical model, radiomic model, combined model. The area under the receiver operating characteristic curve (AUC), accuracy, precision and sensitivity were analyzed to evaluate the performance of these models. RESULTS All of the three models obtained good performances in differentiating benign with malignant solid ovarian tumors in both training and test sets. The AUC, accuracy, precision, sensitivity of clinical model and radiomic model in test set were 0.847 (95% confidence interval (CI), 0.707-0.986, p <0.01), 0.774, 0.769, 0.714, and 0.807 (95%CI, 0.652-0.961, p <0.05), 0.677, 0.643, 0.643, respectively. Combined model had the best prediction results, the AUC, accuracy, precision and sensitivity were 0.954 (95%CI, 0.862-1.0, p <0.01), 0.839, 0.909 and 0.714 in test set. CONCLUSION Radiomics based on machine learning can be helpful for radiologists in differentiating the benign and malignant solid ovarian tumors.
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Affiliation(s)
- Yuemei Zheng
- School of Medical Imaging, Tianjin Medical University, No. 1 Guangdong Road, Tianjin 300203, China
| | - Hong Wang
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Qiong Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Haoran Sun
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Li Guo
- School of Medical Imaging, Tianjin Medical University, No. 1 Guangdong Road, Tianjin 300203, China.
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A Survey on Deep Learning for Precision Oncology. Diagnostics (Basel) 2022; 12:diagnostics12061489. [PMID: 35741298 PMCID: PMC9222056 DOI: 10.3390/diagnostics12061489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 12/27/2022] Open
Abstract
Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.
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Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A, Palumbo P, Bruno F, Grassi R, Giovagnoni A, Grassi R, Miele V, Barile A. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 2022; 40:919-929. [PMID: 35344132 DOI: 10.1007/s11604-022-01271-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 12/21/2022]
Abstract
BACKGROUND Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye. METHODS This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed. RESULTS Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting. CONCLUSIONS Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.
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Affiliation(s)
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy.
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Silvia Pradella
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Alessandra Bruno
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100, L'Aquila, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
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Viganò L, Jayakody Arachchige VS, Fiz F. Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence. World J Gastroenterol 2022; 28:608-623. [PMID: 35317421 PMCID: PMC8900542 DOI: 10.3748/wjg.v28.i6.608] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/29/2021] [Accepted: 01/20/2022] [Indexed: 02/06/2023] Open
Abstract
The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.
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Affiliation(s)
- Luca Viganò
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Visala S Jayakody Arachchige
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Francesco Fiz
- Nuclear Medicine, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
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Rompianesi G, Pegoraro F, Ceresa CDL, Montalti R, Troisi RI. Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J Gastroenterol 2022; 28:108-122. [PMID: 35125822 PMCID: PMC8793013 DOI: 10.3748/wjg.v28.i1.108] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/12/2021] [Accepted: 12/25/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients' survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients' exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies' role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.
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Affiliation(s)
- Gianluca Rompianesi
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
| | - Francesca Pegoraro
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
| | - Carlo DL Ceresa
- Department of Hepato-Pancreato-Biliary Surgery, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9ES, United Kingdom
| | - Roberto Montalti
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Public Health, Federico II University Hospital, Naples 80125, Italy
| | - Roberto Ivan Troisi
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
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Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A. Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 2021; 16:39. [PMID: 34078424 PMCID: PMC8173908 DOI: 10.1186/s13027-021-00379-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/12/2021] [Indexed: 02/06/2023] Open
Abstract
Background Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. Methods The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. Results We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. Conclusions In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
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Affiliation(s)
- Vincenza Granata
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Roberta Fusco
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy.
| | - Maria Luisa Barretta
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Carmine Picone
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
| | - Antonio Avallone
- Abdominal Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Andrea Belli
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Renato Patrone
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Marilina Ferrante
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Diletta Cozzi
- Division of Radiology, "Azienda Ospedaliera Universitaria Careggi", Florence, Italy
| | - Roberta Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Roberto Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy.,Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy
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Guo W, Gu X, Fang Q, Li Q. Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs. Radiol Phys Technol 2020; 14:6-15. [PMID: 32918159 DOI: 10.1007/s12194-020-00584-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 12/27/2022]
Abstract
Conventional machine learning-based methods have been effective in assisting physicians in making accurate decisions and utilized in computer-aided diagnosis for more than 30 years. Recently, deep learning-based methods, and convolutional neural networks in particular, have rapidly become preferred options in medical image analysis because of their state-of-the-art performance. However, the performances of conventional and deep learning-based methods cannot be compared reliably because of their evaluations on different datasets. Hence, we developed both conventional and deep learning-based methods for lung vessel segmentation and chest radiograph registration, and subsequently compared their performances on the same datasets. The results strongly indicated the superiority of deep learning-based methods over their conventional counterparts.
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Affiliation(s)
- Wei Guo
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
- School of Computer, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China
| | - Xiaomeng Gu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qiming Fang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qiang Li
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
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