- •Two radiomic features were identified as response predictors in rectal cancer.
- •This study aims to validate such features on an external larger dataset.
- •The analysis was performed considering clinical and pathological complete response.
- •The variation of length least showed good performance in predicting both outcomes.
- •Grey Level non uniformity reported limited performance as predictor.
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Physica Medica: European Journal of Medical Physics
- Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.Sci Rep. 2017; 7https://doi.org/10.1038/s41598-017-00665-z
- The role of delta radiomics in gastric cancer.Quant Imaging Med Surg. 2018; 8: 719-721
- Delta Radiomics for rectal cancer response prediction with hybrid 0.35 T Magnetic Resonance guided Radiotherapy (MRgRT): a hypothesis generating study for an innovative personalized medicine approach.Radiol Med (Torino). 2018;
- Delta radiomics analysis for local control prediction in pancreatic cancer patients treated using magnetic resonance guided radiotherapy.Diagnostics. 2021; 11: 72https://doi.org/10.3390/diagnostics11010072
- Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images.Radiother Oncol. 2017; 123: 363-369https://doi.org/10.1016/j.radonc.2017.04.016
- On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers.Med Phys. 2017; 44: 1755-1770https://doi.org/10.1002/mp.12188
- Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening.IEEE Access. 2018; 6: 77796-77806https://doi.org/10.1109/ACCESS.2018.2884126
- A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer.Radiol Med. 2021; 126: 421-429https://doi.org/10.1007/s11547-020-01266-z
- MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy.Radiology. 2018; 287: 833-843https://doi.org/10.1148/radiol.2018172300
- Magnetic resonance, vendor-independent, intensity histogram analysis predicting pathologic complete response after radiochemotherapy of rectal cancer.Int J Radiat Oncol Biol Phys. 2018; 102: 765-774https://doi.org/10.1016/j.ijrobp.2018.04.065
- Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer.Radiol Med. 2018; 123: 286-295https://doi.org/10.1007/s11547-017-0838-3
- A TCP-based early regression index predicts the pathological response in neo-adjuvant radio-chemotherapy of rectal cancer.Radiother Oncol. 2018; 128: 564-568https://doi.org/10.1016/j.radonc.2018.06.019
- Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer.Radiat Oncol. 2019; 14https://doi.org/10.1186/s13014-019-1246-8
- MR-guided radiotherapy in rectal cancer: first clinical experience of an innovative technology.Clin Transl Radiat Oncol. 2019; 18: 80-86https://doi.org/10.1016/j.ctro.2019.04.006
- International consensus guidelines on Clinical Target Volume delineation in rectal cancer.Radiother Oncol. 2016; 120: 195-201https://doi.org/10.1016/j.radonc.2016.07.017
- Hybrid Tri-Co-60 MRI radiotherapy for locally advanced rectal cancer: an in silico evaluation.Tech Innov Patient Support Radiat Oncol. 2018; 6: 5-10https://doi.org/10.1016/j.tipsro.2018.02.002
- Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinomaClinicopathologic correlations.Cancer. 1994; 73: 2680-2686https://doi.org/10.1002/1097-0142(19940601)73:11<2680::AID-CNCR2820731105>3.0.CO;2-C
- An introduction to error analysis: The Study of uncertainties in physical measurements.II. University Science Books, II. Sausalito, CA1997
- 21 years of biologically effective dose.Br J Radiol. 2010; 83: 554-568https://doi.org/10.1259/bjr/31372149
- External Validation of Early Regression Index (ERITCP) as predictor of pathologic complete response in rectal cancer using magnetic resonance-guided radiation therapy.Int J Radiat Oncol Biol Phys. 2020; 108: 1347-1356https://doi.org/10.1016/j.ijrobp.2020.07.2323
- State of the art on dose prescription, reporting and recording in Intensity-Modulated Radiation Therapy (ICRU report No. 83).Cancer Radiother. 2011; 15: 555-559https://doi.org/10.1016/j.canrad.2011.04.003
- Towards a modular decision support system for radiomics: a case study on rectal cancer.Artif Intell Med. 2019; 96: 145-153https://doi.org/10.1016/j.artmed.2018.09.003
- pROC: an open-source package for R and S+ to analyze and compare ROC curves.BMC Bioinf. 2011; 12https://doi.org/10.1186/1471-2105-12-77
- Youden index and optimal cut-point estimated from observations affected by a lower limit of detection.Biom J Biom Z. 2008; 50: 419-430https://doi.org/10.1002/bimj.200710415
International Commissioning on Radiation Units and Measurements. Receiver Operating Characteristic (ROC) Analysis in Medical Imaging. ICRU Report 79; 2008.
- Online adaptive magnetic resonance guided radiotherapy for pancreatic cancer: state of the art, pearls and pitfalls.Radiat Oncol. 2019; 14: 71https://doi.org/10.1186/s13014-019-1275-3
- Predicting tumour motion during the whole radiotherapy treatment: a systematic approach for thoracic and abdominal lesions based on real time MR.Radiother Oncol. 2018; 129: 456-462https://doi.org/10.1016/j.radonc.2018.07.025
- Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: aa pilot study.Med Phys. 2020; 47: 3682-3690https://doi.org/10.1002/mp.v47.810.1002/mp.14200
- Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs.Phys Med Biol. 2020; 65https://doi.org/10.1088/1361-6560/ab9e58
- Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.Ann Intern Med. 2015; 162: 55-63https://doi.org/10.7326/M14-0697
- Accurate outcome prediction after neo-adjuvant radio-chemotherapy for rectal cancer based on a TCP-based early regression index.Clin Transl Radiat Oncol. 2019; 19: 12-16https://doi.org/10.1016/j.ctro.2019.07.001
- Effects of MRI image normalization techniques in prostate cancer radiomics.Phys Med. 2020; 71: 7-13https://doi.org/10.1016/j.ejmp.2020.02.007