Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Evidence Acquisition
2.2. Texture Analysis: Workflow and Definition
- (a)
- Shape features: these features refer to the geometric properties of the ROI (volume, diameter, sphericity, and compacity);
- (b)
- Histogram-based features: these features are calculated from the general histogram of the Hounsfield Unit (HU) of the ROI, such as the mean, median, skewness, and kurtosis. These features do not consider the spatial orientation of the voxels;
- (c)
- Second-order texture features: these features consider the statistical relationship between neighboring voxels or groups of voxels within the segmented lesion. These features can be extracted from several matrices, such as the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GRLLM), and neighborhood gray-level different matrix (NGLDM);
- (d)
- Higher-order texture features: these parameters use additional image filters, using specific mathematical transformations that can highlight specific aspects of the ROI. The filter used can be wavelet or Fourier transforms, fractal analysis, and Laplacian of Gaussian;
3. Results
3.1. Texture Analysis in Neoadjuvant Radiotherapy—A Focus on Esophageal Cancer
3.2. Texture Analysis in Neoadjuvant Radiotherapy—A Focus on Lung Cancer
3.3. Texture Analysis in Neoadjuvant Radiotherapy—A Focus on Sarcoma
3.4. Texture Analysis in Neoadjuvant Radiotherapy—A Focus on Rectal Cancer
4. Future Directions and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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NCT Number | Study Type | Cancer Type | Trial Design | Contacts and Locations | Trial Design |
---|---|---|---|---|---|
NCT02439086 | Interventional | Rectal Cancer | 18F-FDG-PET-CT and texture analysis of MRI performed 9 weeks after Neoadjuvant Chemo-radiotherapy in patients with locally advanced rectal cancer to test the ability to identify patients with Complete Response. | Medhat S Alaker, Colchester General Hospital, UK | Single arm, patients will have 2 PET CT scans: one before radiotherapy, and one 9 weeks after. |
NCT04273477 | Observational | Rectal cancer | Radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered. | Xiangbo Wan, Sun Yat-sen University, China | Multicenter, prospective, observational clinical study, evaluating MRI performed before nCRT. |
NCT03238885 | Observational | Rectal cancer | Develop and validate a radiomics model for individualized pCR evaluation after CRT in patients. The ultimate aim is to select appropriate LARC patients for omission of surgery. | Sun Ying-Shi, Beijing Cancer Hospital, China | Prospective, observational cohort study, investigating 3T MRI performed before and after nCRT. |
NCT04489368 | Observational | Esophagus Cancer | Develop models to predict pCR based on pre-neoadjuvant imaging modalities | Kundan S Chufal, Rajiv Gandhi Cancer Institute & Research Center, India | Prospective, observational cohort study, investigating imaging performed before nCRT. |
NCT04278274 | Observational | Rectal Cancer | Evaluation of Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer. | Xiangbo Wan, Sun Yat-sen University, China | Multicenter, prospective, observational clinical study, evaluating MRI performed after nCRT and before surgery. |
NCT04815694 | Interventional | Rectal Cancer | Investigate the impact of dose escalation in rectal cancer, identifying the poor responder cases using the early tumor regression index during the course of radiotherapy and increasing the prescribed dose in these patients. Secondary endpoint is prospective validation of delta radiomics MR-guide Radiotherapy model | Giuditta Chiloiro, Fondazione Policlinico Universitario A.Gemelli IRCCS, Italy | Interventional, two arms. In experimental arm RT Dose escalation will be performed in patients based on Early Regression Index values calculated at second week on nCRT |
NCT04359732 | Interventional | Esophagus Cancer | Prediction of Assessment of Response to Neoadjuvant Chemo-Radio-Therapy (nCRT) for Esophageal and Gastroesophageal Junction Cancer (GEJ) Using a Fully Integrated PET/MRI | Francesco De Cobelli, IRCCS San Raffaele, Italy | Interventional, single arm. An additional intermediate 18-FDG PET/MRI will be performed during nCRT. |
NCT03237130 | Observational | Esophagus Cancer | Establishment of an image feature extraction and selection method for identifying lymph node metastasis of esophageal cancer | Ying-Shi Sun, Dept.Radiology, Peking University Cancer Hospital, China | Prospective, observational cohort study. Each patient will receive preoperative enhanced chest CT examination, and their CT images will be used for analysis |
NCT04090450 | Observational | Rectal Cancer | Retrospective study using images acquired routinely for diagnosis of rectal cancer to see if these could be used to predict responses to radiotherapy treatment and if it can, whether the treatment can be optimized to produce better outcome for patients. | Peter Mbanu, University of Manchester, UK | Retrospective, observational cohort study and will recruit patients who have had nCRT for rectal cancer. MR radiomics features will be analyzed. |
NCT03029793 | Observational | Esophagus Cancer | Determine whether combination of molecular and biomarkers with functional imaging can predict pathologic response and clinical outcomes in squamous esophageal cancer patients who undergo trimodal therapy which includes neoadjuvant chemoradiotherapy and surgery | Li-Na Zhao, Air Force Military Medical University, China | Prospective, observational cohort study, investigating imaging performed before nCRT. The study will collect both tissue samples and imaging in locally advanced esophageal cancer patients. |
NCT04207918 | Interventional | Esophagus Cancer | Evaluate the 1-year local tumor control rates after the targeted therapy of intensity-modulated radiation therapy synchronized chemotherapy with nimotuzumab. Secondary texture analysis of CT and MRI simulation imaging in predicting tumor response rate is included. | Wang Xin, Chinese Academy of Medical Sciences, China | Experimental, single arm, nCRT arm receives RT concurrently with S-1 and Nimotuzumab. Texture analysis of CT and/or MRI simulation will be analyzed in predicting tumor response rate and prognosis. |
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Nardone, V.; Boldrini, L.; Grassi, R.; Franceschini, D.; Morelli, I.; Becherini, C.; Loi, M.; Greto, D.; Desideri, I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers 2021, 13, 3590. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13143590
Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers. 2021; 13(14):3590. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13143590
Chicago/Turabian StyleNardone, Valerio, Luca Boldrini, Roberta Grassi, Davide Franceschini, Ilaria Morelli, Carlotta Becherini, Mauro Loi, Daniela Greto, and Isacco Desideri. 2021. "Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment" Cancers 13, no. 14: 3590. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13143590