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Sheen H, Cho W, Kim C, Han MC, Kim H, Lee H, Kim DW, Kim JS, Hong CS. Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis. Phys Med 2024; 123:103414. [PMID: 38906047 DOI: 10.1016/j.ejmp.2024.103414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 06/23/2024] Open
Abstract
PURPOSE This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models. METHODS We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed. RESULTS Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models. CONCLUSIONS Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life. PROTOCOL REGISTRATION The protocol of this study was registered on PROSPERO (CRD42023426565).
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Affiliation(s)
- Heesoon Sheen
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, South Korea
| | - Wonyoung Cho
- Research Institute, Oncosoft Inc., Seoul, South Korea
| | - Changhwan Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Min Cheol Han
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, 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
| | - Ho Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Dong Wook Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Research Institute, Oncosoft Inc., Seoul, South Korea; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
| | - Chae-Seon Hong
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
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Li C, Zhang J, Ning B, Xu J, Lin Z, Zhang J, Tan N, Yu X, Su W, Ni W, Yu W, Wu J, Cao G, Cao Z, Xie C, Jin X. Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy. Radiat Oncol 2024; 19:72. [PMID: 38851718 PMCID: PMC11161999 DOI: 10.1186/s13014-024-02462-1] [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: 11/29/2023] [Accepted: 05/28/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.
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Affiliation(s)
- Chenyu Li
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Ji Zhang
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Boda Ning
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Jiayi Xu
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhixi Lin
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Jicheng Zhang
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Ninghang Tan
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Xianwen Yu
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Wanyu Su
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Weihua Ni
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Cixi Biomedical Research Institute, Wenzhou Medical University, Zhejiang, 315000, China
| | - Wenliang Yu
- Department of Radiation Oncology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People' s Hospital, Quzhou, 324000, China
| | - Jianping Wu
- Department of Radiation Oncology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People' s Hospital, Quzhou, 324000, China
| | - Guoquan Cao
- Radiological Department, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhuo Cao
- Department of Respiratory, Lishui People's Hospital, Lishui, 323000, China.
| | - Congying Xie
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Xiance Jin
- Radiotherapy Center, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, 325000, China.
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Li J, Li L, Tang S, Yu Q, Liu W, Liu N, Yang F, Zhang D, Yuan S. Novel model integrating computed tomography-based image markers with genetic markers for discriminating radiation pneumonitis in patients with unresectable stage III non-small cell lung cancer receiving radiotherapy: a retrospective multi-center radiogenomics study. BMC Cancer 2024; 24:78. [PMID: 38225543 PMCID: PMC10789008 DOI: 10.1186/s12885-023-11809-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/28/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Chemoradiotherapy is a critical treatment for patients with locally advanced and unresectable non-small cell lung cancer (NSCLC), and it is essential to identify high-risk patients as early as possible owing to the high incidence of radiation pneumonitis (RP). Increasing attention is being paid to the effects of endogenous factors for RP. This study aimed to investigate the value of computed tomography (CT)-based radiomics combined with genomics in analyzing the risk of grade ≥ 2 RP in unresectable stage III NSCLC. METHODS In this retrospective multi-center observational study, 100 patients with unresectable stage III NSCLC who were treated with chemoradiotherapy were analyzed. Radiomics features of the entire lung were extracted from pre-radiotherapy CT images. The least absolute shrinkage and selection operator algorithm was used for optimal feature selection to calculate the Rad-score for predicting grade ≥ 2 RP. Genomic DNA was extracted from formalin-fixed paraffin-embedded pretreatment biopsy tissues. Univariate and multivariate logistic regression analyses were performed to identify predictors of RP for model development. The area under the receiver operating characteristic curve was used to evaluate the predictive capacity of the model. Statistical comparisons of the area under the curve values between different models were performed using the DeLong test. Calibration and decision curves were used to demonstrate discriminatory and clinical benefit ratios, respectively. RESULTS The Rad-score was constructed from nine radiomic features to predict grade ≥ 2 RP. Multivariate analysis demonstrated that histology, Rad-score, and XRCC1 (rs25487) allele mutation were independent high-risk factors correlated with RP. The area under the curve of the integrated model combining clinical factors, radiomics, and genomics was significantly higher than that of any single model (0.827 versus 0.594, 0.738, or 0.641). Calibration and decision curve analyses confirmed the satisfactory clinical feasibility and utility of the nomogram. CONCLUSION Histology, Rad-score, and XRCC1 (rs25487) allele mutation could predict grade ≥ 2 RP in patients with locally advanced unresectable NSCLC after chemoradiotherapy, and the integrated model combining clinical factors, radiomics, and genomics demonstrated the best predictive efficacy.
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Affiliation(s)
- Jiaran Li
- Shandong University Cancer Center, Jinan, Shandong, China
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Li Li
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shanshan Tang
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Qingxi Yu
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Wenju Liu
- Department of Radiation Oncology, Liaocheng Pepole's Hospital, Liaocheng, Shandong, China
| | - Ning Liu
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Fengchang Yang
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Dexian Zhang
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shuanghu Yuan
- Shandong University Cancer Center, Jinan, Shandong, China.
- Department of Radiation Oncology, Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China.
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Hassaninejad H, Abdollahi H, Abedi I, Amouheidari A, Tavakoli MB. Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison. Phys Eng Sci Med 2023; 46:1353-1363. [PMID: 37556091 DOI: 10.1007/s13246-023-01260-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/13/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Rectal toxicity is one of the common side effects after radiotherapy in prostate cancer patients. Radiomics is a non-invasive and low-cost method for developing models of predicting radiation toxicity that does not have the limitations of previous methods. These models have been developed using individual patients' information and have reliable and acceptable performance. This study was conducted by evaluating the radiomic features of computed tomography (CT) and magnetic resonance (MR) images and using machine learning (ML) methods to predict radiation-induced rectal toxicity. METHODS Seventy men with pathologically confirmed prostate cancer, eligible for three-dimensional radiation therapy (3DCRT) participated in this prospective trial. Rectal wall CT and MR images were used to extract first-order, shape-based, and textural features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) were used to create models based on radiomic, dosimetric, and clinical data alone or in combination. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess each model's performance. RESULTS The best outcomes were achieved by the radiomic features of MR images in conjunction with clinical and dosimetric data, with a mean of AUC: 0.79, accuracy: 77.75%, specificity: 82.15%, and sensitivity: 67%. CONCLUSIONS This research showed that as radiomic signatures for predicting radiation-induced rectal toxicity, MR images outperform CT images.
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Affiliation(s)
- Hossein Hassaninejad
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | - Hamid Abdollahi
- Department of Radiology Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Iraj Abedi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran
| | | | - Mohamad Bagher Tavakoli
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran.
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Li Z, Raldow AC, Weidhaas JB, Zhou Q, Qi XS. Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT. Cancers (Basel) 2023; 15:5142. [PMID: 37958316 PMCID: PMC10647315 DOI: 10.3390/cancers15215142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/07/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023] Open
Abstract
Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.
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Affiliation(s)
- Zirong Li
- Manteia Medical Technologies Co., Milwaukee, WI 53226, USA;
| | - Ann C. Raldow
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
| | - Joanne B. Weidhaas
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
| | - Qichao Zhou
- Manteia Medical Technologies Co., Milwaukee, WI 53226, USA;
| | - X. Sharon Qi
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
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Zhou G, Yin M, He W, Ma Y, Li C, Li Z, Li X, Wang S, Wu G. Transarterial infusion chemotherapy for advanced esophageal cancer with airway stenosis. Front Oncol 2023; 13:1238287. [PMID: 37727208 PMCID: PMC10505802 DOI: 10.3389/fonc.2023.1238287] [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: 06/11/2023] [Accepted: 08/02/2023] [Indexed: 09/21/2023] Open
Abstract
Purpose This study aimed to investigate the safety and efficacy of transarterial infusion chemotherapy for the treatment of esophageal cancer with airway stenosis. Methods Data of patients with advanced esophageal cancer complicated with airway stenosis treated with transarterial infusion chemotherapy were retrospectively analyzed. Dyspnea, clinical efficacy and adverse reactions were evaluated. Results Of these patients, 27 had grade II preoperative dyspnea, and 31 had grade III preoperative dyspnea, 26 had grade I postoperative dyspnea, 25 had grade II postoperative dyspnea, and 7 had grade III postoperative dyspnea. Among 3 patients with left main bronchial stenosis and atelectasis, 2 had complete remission after transarterial infusion chemotherapy, and 1 demonstrated partial remission. After treatment, complete response, partial response, and stable disease were observed in 7, 34, and 17 cases, respectively. Total objective effective rate and disease control rate were 70.6% (41/58) and 100.0%, respectively. During follow up, 24 patients died of organ failure, and 17 patients died of tumor-related respiratory failure. Seven patients died of gastrointestinal bleeding, 1 patient died of myocardial infarction, and 9 patients survived. Conclusions Transarterial infusion chemotherapy is safe and effective for the treatment of advanced esophageal cancer with airway stenosis.
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Affiliation(s)
- Gang Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Meipan Yin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wei He
- Oncology Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaozhen Ma
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chunxia Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaobing Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shuai Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Gang Wu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Zhang Z, Wang Z, Yan M, Yu J, Dekker A, Zhao L, Wee L. Radiomics and Dosiomics Signature From Whole Lung Predicts Radiation Pneumonitis: A Model Development Study With Prospective External Validation and Decision-curve Analysis. Int J Radiat Oncol Biol Phys 2023; 115:746-758. [PMID: 36031028 DOI: 10.1016/j.ijrobp.2022.08.047] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/10/2022] [Accepted: 08/20/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Radiation pneumonitis (RP) is one of the common side effects of radiation therapy in the thoracic region. Radiomics and dosiomics quantify information implicit within medical images and radiation therapy dose distributions. In this study we demonstrate the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction. METHODS AND MATERIALS Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively collected and 35 prospectively enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R score) and dosiomics risk score (D score), as well as a DVH-score, were calculated based on logistic regression after feature selection. Six models were built using different combinations of R score, D score, DVH score, and clinical parameters to evaluate their added prognostic power. Overoptimism was evaluated by bootstrap resampling from the training set, and the prospectively collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models. RESULTS A model built by integrating all of the R score, D score, and clinical parameters had the best discriminative ability with areas under the curve of 0.793 (95% confidence interval [CI], 0.735-0.851), 0.774 (95% CI, 0.762-0.786), and 0.855 (95% CI, 0.719-0.990) in the training, bootstrapping, and external test sets, respectively. The calibration curve image showed good agreement between the predicted and actual values, with a slope of 1.21 and intercept of -0.04. The decision curve image showed a positive net benefit for the final model based on the nomogram. CONCLUSIONS Radiomic and dosiomic features have the potential to assist with the prediction of RP, and the combination of radiomics, dosiomics, and clinical parameters led to the best prognostic model in the present study.
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Affiliation(s)
- Zhen Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Zhixiang Wang
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Meng Yan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jiaqi Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Andre Dekker
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Leonard Wee
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
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Zhang Z, Wang Z, Luo T, Yan M, Dekker A, De Ruysscher D, Traverso A, Wee L, Zhao L. Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy. Radiother Oncol 2023; 182:109581. [PMID: 36842666 DOI: 10.1016/j.radonc.2023.109581] [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: 08/23/2022] [Revised: 02/17/2023] [Accepted: 02/17/2023] [Indexed: 02/28/2023]
Abstract
PURPOSE To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in lung cancer patients who received radical (chemo)radiotherapy. METHODS CT, RD images and clinical parameters were obtained from 314 retrospectively-collected patients (training set) and 35 prospectively-collected patients (test-set-1) who were diagnosed with lung cancer and received radical radiotherapy in the dose range of 50 Gy and 70 Gy. Another 194 (60 Gy group, test-set-2) and 158 (74 Gy group, test-set-3) patients from the clinical trial RTOG 0617 were used for external validation. A ResNet architecture was used to develop a prediction model that combines CT and RD features. Thereafter, the CT and RD weights were adjusted by using 40 patients from test-set-2 or 3 to accommodate cohorts with different clinical settings or dose delivery patterns. Visual interpretation was implemented using a gradient-weighted class activation map (grad-CAM) to observe the area of model attention during the prediction process. To improve the usability, ready-to-use online software was developed. RESULTS The discriminative ability of a baseline trained model had an AUC of 0.83 for test-set-1, 0.55 for test-set-2, and 0.63 for test-set-3. After adjusting CT and RD weights of the model using a subset of the RTOG-0617 subjects, the discriminatory power of test-set-2 and 3 improved to AUC 0.65 and AUC 0.70, respectively. Grad-CAM showed the regions of interest to the model that contribute to the prediction of RP. CONCLUSION A novel deep learning approach combining CT and RD images can effectively and accurately predict the occurrence of RP, and this model can be adjusted easily to fit new cohorts.
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Affiliation(s)
- Zhen Zhang
- Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. 310022; Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. 6229 ET
| | - Zhixiang Wang
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. 6229 ET; Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tianchen Luo
- Institute of System Science, National University of Singapore, Singapore. 119260
| | - Meng Yan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, China. 300060
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. 6229 ET
| | - Dirk De Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. 6229 ET
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. 6229 ET
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. 6229 ET.
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, China. 300060.
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Takahashi N, Tanaka S, Umezawa R, Takanami K, Takeda K, Yamamoto T, Suzuki Y, Katsuta Y, Kadoya N, Jingu K. Development and validation of an [ 18F]FDG-PET/CT radiomic model for predicting progression-free survival for patients with stage II - III thoracic esophageal squamous cell carcinoma who are treated with definitive chemoradiotherapy. Acta Oncol 2023; 62:159-165. [PMID: 36794365 DOI: 10.1080/0284186x.2023.2178859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
BACKGROUND Radiomics is a method for extracting a large amount of information from images and used to predict treatment outcomes, side effects and diagnosis. In this study, we developed and validated a radiomic model of [18F]FDG-PET/CT for predicting progression-free survival (PFS) of definitive chemoradiotherapy (dCRT) for patients with esophageal cancer. MATERIAL AND METHODS Patients with stage II - III esophageal cancer who underwent [18F]FDG-PET/CT within 45 days before dCRT between 2005 and 2017 were included. Patients were randomly assigned to a training set (85 patients) and a validation set (45 patients). Radiomic parameters inside the area of standard uptake value ≥ 3 were calculated. The open-source software 3D slicer and Pyradiomics were used for segmentation and calculating radiomic parameters, respectively. Eight hundred sixty radiomic parameters and general information were investigated.In the training set, a radiomic model for PFS was made from the LASSO Cox regression model and Rad-score was calculated. In the validation set, the model was applied to Kaplan-Meier curves. The median value of Rad-score in the training set was used as a cutoff value in the validation set. JMP was used for statistical analysis. RStudio was used for the LASSO Cox regression model. p < 0.05 was defined as significant. RESULTS The median follow-up periods were 21.9 months for all patients and 63.4 months for survivors. The 5-year PFS rate was 24.0%. In the training set, the LASSO Cox regression model selects 6 parameters and made a model. The low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.019). In the validation set, the low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.040). CONCLUSIONS The [18F]FDG-PET/CT radiomic model could predict PFS for patients with esophageal cancer who received dCRT.
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Affiliation(s)
- Noriyoshi Takahashi
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Kentaro Takanami
- Department of Radiology, Tohoku University Graduate School of Medicine
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Yu Suzuki
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine
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Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy. Cancers (Basel) 2023; 15:cancers15041105. [PMID: 36831445 PMCID: PMC9954441 DOI: 10.3390/cancers15041105] [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: 12/10/2022] [Revised: 01/27/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.
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12
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Tong Y, Li J, Chen J, Hu C, Xu Z, Duan S, Wang X, Yu R, Cheng X. A Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperative Prediction of DNA Mismatch Repair Deficiency in Gastric Adenocarcinoma. Front Oncol 2022; 12:865548. [PMID: 35912185 PMCID: PMC9327646 DOI: 10.3389/fonc.2022.865548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/26/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram integrated with clinic-radiological features for preoperative prediction of DNA mismatch repair deficiency (dMMR) in gastric adenocarcinoma. Materials and Methods From March 2014 to August 2020, 161 patients with pathologically confirmed gastric adenocarcinoma were included from two centers (center 1 as the training and internal testing sets, n = 101; center 2 as the external testing sets, n = 60). All patients underwent preoperative contrast-enhanced computerized tomography (CT) examination. Radiomics features were extracted from portal-venous phase CT images. Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used to select features, and then radiomics signature was constructed using logistic regression analysis. A radiomics nomogram was built incorporating the radiomics signature and independent clinical predictors. The model performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA). Results The radiomics signature, which was constructed using two selected features, was significantly associated with dMMR gastric adenocarcinoma in the training and internal testing sets (P < 0.05). The radiomics signature model showed a moderate discrimination ability with an area under the ROC curve (AUC) of 0.81 in the training set, which was confirmed with an AUC of 0.78 in the internal testing set. The radiomics nomogram consisting of the radiomics signature and clinical factors (age, sex, and location) showed excellent discrimination in the training, internal testing, and external testing sets with AUCs of 0.93, 0.82, and 0.83, respectively. Further, calibration curves and DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram. Conclusions The radiomics nomogram combining radiomics signature and clinical characteristics (age, sex, and location) may be used to individually predict dMMR of gastric adenocarcinoma.
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Affiliation(s)
- Yahan Tong
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Jiaying Li
- Department of Radiology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jieyu Chen
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Can Hu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Zhiyuan Xu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Shaofeng Duan
- Precision Health Institution, GE Healthcare, Shanghai, China
| | - Xiaojie Wang
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Risheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Xiangdong Cheng, ; Risheng Yu,
| | - Xiangdong Cheng
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- *Correspondence: Xiangdong Cheng, ; Risheng Yu,
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Qiu Q, Xing L, Wang Y, Feng A, Wen Q. Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer. Front Immunol 2022; 13:870842. [PMID: 35558076 PMCID: PMC9088878 DOI: 10.3389/fimmu.2022.870842] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/24/2022] [Indexed: 01/27/2023] Open
Abstract
Background The combination of immunotherapy and chemoradiotherapy has become the standard therapeutic strategy for patients with unresected locally advance-stage non-small cell lung cancer (NSCLC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by pretreatment CT radiomics and clinical or radiological parameters. Methods A total of 126 advance-stage NSCLC patients with pneumonitis were enrolled in this retrospective study and divided into the training dataset (n =88) and the validation dataset (n = 38). A total of 837 radiomics features were extracted from regions of interest based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator. A logistic regression was applied to develop a radiomics nomogram. Receiver operating characteristics curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification. Results There was no significant difference between the training and the validation datasets for any clinicopathological parameters in this study. The radiomics signature, named Rad-score, consisting of 11 selected radiomics features, has potential ability to differentiate between CIP and RP with the empirical and α-binormal-based AUCs of 0.891 and 0.896. These results were verified in the validation dataset with AUC = 0.901 and 0.874, respectively. The clinical and radiological parameters of bilateral changes (p < 0.001) and sharp border (p = 0.001) were associated with the identification of CIP and RP. The nomogram model showed good performance on discrimination in the training dataset (AUC = 0.953 and 0.950) and in the validation dataset (AUC = 0.947 and 0.936). Conclusions CT-based radiomics features have potential values for differentiating between patients with CIP and patients with RP. The addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.
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Affiliation(s)
- Qingtao Qiu
- Department of Radiation Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yu Wang
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, China
| | - Alei Feng
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, China
| | - Qiang Wen
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, China
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Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022; 42:1-13. [PMID: 35671432 DOI: 10.1200/edbk_350931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.
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Affiliation(s)
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH
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Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021; 49:2462-2481. [PMID: 34939174 PMCID: PMC9206619 DOI: 10.1007/s00259-021-05658-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/12/2021] [Indexed: 10/24/2022]
Abstract
PURPOSE Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. METHODS A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. RESULTS Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. CONCLUSION A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
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Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study. Cancers (Basel) 2021; 13:cancers13092145. [PMID: 33946826 PMCID: PMC8124289 DOI: 10.3390/cancers13092145] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/23/2021] [Accepted: 04/26/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. METHODS We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. RESULTS The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. CONCLUSIONS Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
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Zhao C, Zhang Z, Hu X, Zhang L, Liu Y, Wang Y, Guo Y, Zhang T, Li W, Li B. Hyaluronic Acid Correlates With Bone Metastasis and Predicts Poor Prognosis in Small-Cell Lung Cancer Patients. Front Endocrinol (Lausanne) 2021; 12:785192. [PMID: 35154001 PMCID: PMC8826575 DOI: 10.3389/fendo.2021.785192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/24/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Hyaluronan (HA) is one of the essential elements of the extracellular matrix (ECM), involved in the onset of metastasis in various tumors. The interaction and binding of the ligand-receptor HA/cluster of differentiation-44 (CD44) regulate the physical and biochemical properties of the ECM, which correlates with an increased propensity toward metastasis and poor survival outcome. Our study aimed to explore HA for predicting metastasis and survival rate in patients with small-cell lung cancer (SCLC). MATERIALS AND METHODS This prospective cohort study recruited 72 patients with SCLC. Plasma HA and CD44 levels were assayed by enzyme-linked immunosorbent assay (ELISA) for 72 cases before initial systematic treatment (baseline samples), and plasma HA was detected via after-2-cycle-chemotherapy (A-2-C-CT) in 48 samples. Logistic regression analysis and the Cox proportional risk model were used to determine the independent predictors of distant metastasis and survival rate of patients. RESULTS Baseline plasma HA was notably associated with bone metastasis (BM) [OR (95% CI = 1.015 (1.006-1.024), p = 0.001]. Multivariate logistic regression analysis showed that baseline plasma HA was chosen as an independent predictor of BM. Either baseline HA or CD44 or both were associated with BM. Dynamic alteration of HA was notably associated with A-2-C-CT clinical efficacy. Multivariate Cox regression analysis in forward likelihood ratio showed that A-2-C-CT HA was an independent predictor of progression-free survival (PFS) and overall survival (OS). CONCLUSIONS HA appears to be used as an independent predictive factor for BM, and the dynamic detection of HA can predict prognosis in SCLC patients. The mechanism of the HA/CD44 axis in BM of SCLC deserves further exploration.
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Affiliation(s)
- Cong Zhao
- Department of Cellular and Molecular Biology, Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Beijing, China
- General Department, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Zhiyun Zhang
- Department of Cellular and Molecular Biology, Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Beijing, China
- General Department, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Xingsheng Hu
- Department of Medical Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Department of Cellular and Molecular Biology, Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Beijing, China
| | - Yanxia Liu
- Department of Cellular and Molecular Biology, Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Beijing, China
- General Department, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Ying Wang
- General Department, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Yi Guo
- Department of Cellular and Molecular Biology, Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Beijing, China
- General Department, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Tongmei Zhang
- General Department, Beijing Chest Hospital, Capital Medical University, Beijing, China
- *Correspondence: Weiying Li, ; Tongmei Zhang, ; Baolan Li,
| | - Weiying Li
- Department of Cellular and Molecular Biology, Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Beijing, China
- *Correspondence: Weiying Li, ; Tongmei Zhang, ; Baolan Li,
| | - Baolan Li
- General Department, Beijing Chest Hospital, Capital Medical University, Beijing, China
- *Correspondence: Weiying Li, ; Tongmei Zhang, ; Baolan Li,
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