Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment

Treatment outcomes and patterns of radiologic appearance after hypofractionated image-guided radiotherapy delivered with helical tomotherapy (HHT) for lung tumours

“…Novel metrics of assessing tumour response after SABR based on advanced quantitative radiomic analysis on CT imaging have demonstrated to be more accurate in distinguishing benign fibrotic changes from local recurrence than the use of RECIST alone or in combination with functional imaging. 41,42 Furthermore, we did not attempt to provide a differential diagnosis between recurrence and second primary lung cancers in patients who underwent HHT. Actually, owing to the short median FU of this series, we could not apply the criteria of Martini and Melamed, 43 and we assumed as a conventional wisdom that recurrences generally occur during the first 2 years after treatment.…”

Texture analysis of medical images for radiotherapy applications

“…6 Although most parts of the works in this context faced the detection of RILI by the evaluation of simple CT ; Pota et al, 2015 103 CRT, chemoradiotherapy; DCE-MRI, dynamic contrast-enhanced MRI; EBRT, external beam radiotherapy; IMRT, intensity-modulated radiotherapy; mp-MRI, multiparametric MRI; PET, positron emission tomography; RT, radiotherapy; SABR, stereotactic ablative radiation therapy; SRT, stereotactic radiotherapy; T2w, T 2 weighted. density, [76][77][78] Mattonen et al [79][80][81] proposed texture analysis for an automatic classification of tumour recurrence and lung injuries. They found a radiomic signature consisting of five textural features (minimum grey level, grey-level uniformity, GLCM homogeneity, GLCM correlation and GLCM energy) after stereotactic ablative radiation therapy (SABR) in consolidative and periconsolidative regions.…”

“…They compared these results with performance of three radiation oncologists and three radiologists (mean error of 35%, falsepositive rate of 1% and false-negative rate of 99%), suggesting that texture analysis can detect early changes associated with local recurrence that are not typically considered by physicians. 81 Other works focused on prediction of lung recurrence. For example, from PET images, it was found that heterogeneity measures, such as entropy, can predict diseasespecific survival, 82,83 whereas CT images were used for the assessment of pathologic response, 84 overall survival and distant metastases, 85 finding that texture analysis can outperform conventional indices (as tumour volume and diameter).…”

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

“…Wavelet and textural features were found to be overexpressing in patients with distant metastasis (confidence interval of 0.67) that failed stereotactic body radiation therapy, whereas clinical and conventional parameters failed to be predictive in these patients. Meanwhile, Mationen et al 124 showed the significance of radio-mic textural features, including gray-level co-occurrence matrices and gray-level features, which were intensified in patients who recurred even after radiation therapy in early-stage NSCLC. The radiomic features were predictive of recurrence (AUC of 0.85) after radiation.…”