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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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Guo W, Li B, Xu W, Cheng C, Qiu C, Sam SK, Zhang J, Teng X, Meng L, Zheng X, Wang Y, Lou Z, Mao R, Lei H, Zhang Y, Zhou T, Li A, Cai J, Ge H. Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy. J Cancer Res Clin Oncol 2024; 150:39. [PMID: 38280037 PMCID: PMC10821966 DOI: 10.1007/s00432-023-05520-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/20/2023] [Indexed: 01/29/2024]
Abstract
OBJECTIVE This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). METHODS We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. RESULTS For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). CONCLUSION Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.
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Affiliation(s)
- Wei Guo
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Bing Li
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Wencai Xu
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Chen Cheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Chengyu Qiu
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Sai-Kit Sam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lingguang Meng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Xiaoli Zheng
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Yuan Wang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Zhaoyang Lou
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Ronghu Mao
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Hongchang Lei
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, China
| | - Ta Zhou
- School of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Aijia Li
- Zhengzhou University School of Medicine, Zhengzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, 127 Dong Ming Rd, Zhengzhou, Henan Province, China.
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3
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Teng X, Zhang J, Zhang X, Fan X, Zhou T, Huang YH, Wang L, Lee EYP, Yang R, Cai J. Noninvasive imaging signatures of HER2 and HR using ADC in invasive breast cancer: repeatability, reproducibility, and association with pathological complete response to neoadjuvant chemotherapy. Breast Cancer Res 2023; 25:77. [PMID: 37381020 DOI: 10.1186/s13058-023-01674-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND The immunohistochemical test (IHC) of HER2 and HR can provide prognostic information and treatment guidance for invasive breast cancer patients. We aimed to develop noninvasive image signatures ISHER2 and ISHR of HER2 and HR, respectively. We independently evaluate their repeatability, reproducibility, and association with pathological complete response (pCR) to neoadjuvant chemotherapy. METHODS Pre-treatment DWI, IHC receptor status HER2/HR, and pCR to neoadjuvant chemotherapy of 222 patients from the multi-institutional ACRIN 6698 trial were retrospectively collected. They were pre-separated for development, independent validation, and test-retest. 1316 image features were extracted from DWI-derived ADC maps within manual tumor segmentations. ISHER2 and ISHR were developed by RIDGE logistic regression using non-redundant and test-retest reproducible features relevant to IHC receptor status. We evaluated their association with pCR using area under receiver operating curve (AUC) and odds ratio (OR) after binarization. Their reproducibility was further evaluated using the test-retest set with intra-class coefficient of correlation (ICC). RESULTS A 5-feature ISHER2 targeting HER2 was developed (AUC = 0.70, 95% CI 0.59 to 0.82) and validated (AUC = 0.72, 95% CI 0.58 to 0.86) with high perturbation repeatability (ICC = 0.92) and test-retest reproducibility (ICC = 0.83). ISHR was developed using 5 features with higher association with HR during development (AUC = 0.75, 95% CI 0.66 to 0.84) and validation (AUC = 0.74, 95% CI 0.61 to 0.86) and similar repeatability (ICC = 0.91) and reproducibility (ICC = 0.82). Both image signatures showed significant associations with pCR with AUC of 0.65 (95% CI 0.50 to 0.80) for ISHER2 and 0.64 (95% CI 0.50 to 0.78) for ISHER2 in the validation cohort. Patients with high ISHER2 were more likely to achieve pCR to neoadjuvant chemotherapy with validation OR of 4.73 (95% CI 1.64 to 13.65, P value = 0.006). Low ISHR patients had higher pCR with OR = 0.29 (95% CI 0.10 to 0.81, P value = 0.021). Molecular subtypes derived from the image signatures showed comparable pCR prediction values to IHC-based molecular subtypes (P value > 0.05). CONCLUSION Robust ADC-based image signatures were developed and validated for noninvasive evaluation of IHC receptors HER2 and HR. We also confirmed their value in predicting treatment response to neoadjuvant chemotherapy. Further evaluations in treatment guidance are warranted to fully validate their potential as IHC surrogates.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinyu Fan
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Lu Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Elaine Yuen Phin Lee
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Y920, Lee Shau Kee Building, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Hong Kong, China.
- Research Institute for Smart Aging, The Hong Kong Polytechnic University, Hong Kong, China.
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Niu L, Chu X, Yang X, Zhao H, Chen L, Deng F, Liang Z, Jing D, Zhou R. A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04827-7. [PMID: 37154927 DOI: 10.1007/s00432-023-04827-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 04/28/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival. METHODS This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method. RESULTS Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001). CONCLUSIONS The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.
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Affiliation(s)
- Lishui Niu
- Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan, China
| | - Xianjing Chu
- Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan, China
| | - Xianghui Yang
- Department of Oncology, The Affiliated Changsha Central Hospital, Henyang Medical School, University of South China, Changsha, 410004, China
| | - Hongxiang Zhao
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100000, China
| | - Liu Chen
- Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan, China
| | - Fuxing Deng
- Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan, China
| | - Zhan Liang
- Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan, China
| | - Di Jing
- Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan, China.
| | - Rongrong Zhou
- Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Kaifu District, Changsha, 410008, Hunan, China.
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
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Dong Y, Zhang J, Lam S, Zhang X, Liu A, Teng X, Han X, Cao J, Li H, Lee FK, Yip CW, Au K, Zhang Y, Cai J. Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy. Cancers (Basel) 2023; 15:cancers15072032. [PMID: 37046693 PMCID: PMC10093711 DOI: 10.3390/cancers15072032] [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: 02/23/2023] [Revised: 03/21/2023] [Accepted: 03/26/2023] [Indexed: 04/14/2023] Open
Abstract
(1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine. (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction.
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Affiliation(s)
- Yanjing Dong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Saikt Lam
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinyu Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Anran Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jin Cao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hongxiang Li
- Department of Radiology, Fujian Medical University Union Hospital, Fujian Medical University, Fuzhou 350000, China
| | - Francis Karho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Celia Waiyi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Kwokhung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong 226000, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518000, China
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Petrella F, Rizzo S, Attili I, Passaro A, Zilli T, Martucci F, Bonomo L, Del Grande F, Casiraghi M, De Marinis F, Spaggiari L. Stage III Non-Small-Cell Lung Cancer: An Overview of Treatment Options. Curr Oncol 2023; 30:3160-3175. [PMID: 36975452 PMCID: PMC10047909 DOI: 10.3390/curroncol30030239] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/27/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
Lung cancer is the second-most commonly diagnosed cancer and the leading cause of cancer death worldwide. The most common histological type is non-small-cell lung cancer, accounting for 85% of all lung cancer cases. About one out of three new cases of non-small-cell lung cancer are diagnosed at a locally advanced stage—mainly stage III—consisting of a widely heterogeneous group of patients presenting significant differences in terms of tumor volume, local diffusion, and lymph nodal involvement. Stage III NSCLC therapy is based on the pivotal role of multimodal treatment, including surgery, radiotherapy, and a wide-ranging option of systemic treatments. Radical surgery is indicated in the case of hilar lymphnodal involvement or single station mediastinal ipsilateral involvement, possibly after neoadjuvant chemotherapy; the best appropriate treatment for multistation mediastinal lymph node involvement still represents a matter of debate. Although the main scope of treatments in this setting is potentially curative, the overall survival rates are still poor, ranging from 36% to 26% and 13% in stages IIIA, IIIB, and IIIC, respectively. The aim of this article is to provide an up-to-date, comprehensive overview of the state-of-the-art treatments for stage III non-small-cell lung cancer.
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Affiliation(s)
- Francesco Petrella
- Department of Thoracic Surgery, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
- Correspondence: ; Tel.: +0039-0257489362
| | - Stefania Rizzo
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), EOC, Via Tesserete 46, 6900 Lugano, Switzerland
- Faculty of Biomedical Sciences, University of Italian Switzerland, Via Buffi 13, 6900 Lugano, Switzerland
| | - Ilaria Attili
- Division of Thoracic Oncology, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antonio Passaro
- Division of Thoracic Oncology, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Thomas Zilli
- Faculty of Biomedical Sciences, University of Italian Switzerland, Via Buffi 13, 6900 Lugano, Switzerland
- Radiation Oncology, Oncological Institute of Southern Switzerland, EOC, 6500 Bellinzona, Switzerland
- Faculty of Medicine, University of Geneva, 1211 Geneva, Switzerland
| | - Francesco Martucci
- Radiation Oncology, Oncological Institute of Southern Switzerland, EOC, 6500 Bellinzona, Switzerland
| | - Luca Bonomo
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), EOC, Via Tesserete 46, 6900 Lugano, Switzerland
| | - Filippo Del Grande
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), EOC, Via Tesserete 46, 6900 Lugano, Switzerland
- Faculty of Biomedical Sciences, University of Italian Switzerland, Via Buffi 13, 6900 Lugano, Switzerland
| | - Monica Casiraghi
- Department of Thoracic Surgery, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
| | - Filippo De Marinis
- Division of Thoracic Oncology, European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenzo Spaggiari
- Department of Thoracic Surgery, European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20141 Milan, Italy
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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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