Abstract
PurposeAim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner.MethodsIn two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with Z-score normalisation and an initial feature selection was carried out using Wilcoxon–Mann–Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness.ResultsThree features were selected: maximum fractal dimension with IB = 0–50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0–50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively.ConclusionsThe model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.
Highlights
Rectal cancer accounts for one third of colorectal cancers and is to date one of the leading causes of cancer death in the western world [1, 2].Neoadjuvant chemo-radiotherapy followed by total mesorectal excision (TME) represents the standard of care for patients affected by locally advanced rectal cancer (LARC), defined as stage II (T3 or T4, node-negative, M0) and stage III (T3 or T4, node-positive, M0) rectal cancer.1 3 Vol.:(0123456789)La radiologia medica (2021) 126:421–429In particular, nCRT reduces the risk of local recurrence and downsizes the primary tumour facilitating the subsequent successful surgical resection or allowing sphincter-preserving approaches
This study demonstrates that by using appropriate feature selection methods, it is possible to elaborate predictive models able to overcome the variability due to different magnetic field intensities
A MR radiomics prediction model for pathological complete response (pCR) after neoadjuvant therapy in locally advanced rectal cancer was developed: the model showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged
Summary
Rectal cancer accounts for one third of colorectal cancers and is to date one of the leading causes of cancer death in the western world [1, 2].Neoadjuvant chemo-radiotherapy (nCRT) followed by total mesorectal excision (TME) represents the standard of care for patients affected by locally advanced rectal cancer (LARC), defined as stage II (T3 or T4, node-negative, M0) and stage III (T3 or T4, node-positive, M0) rectal cancer.1 3 Vol.:(0123456789)La radiologia medica (2021) 126:421–429In particular, nCRT reduces the risk of local recurrence and downsizes the primary tumour facilitating the subsequent successful surgical resection or allowing sphincter-preserving approaches. More conservative respect to the TME surgery, have currently under investigation for patients showed pCR after nCRT, such as local excision (LE) or watch and wait (W&W) [9,10,11]. To increase the number of locally advanced rectal cancer (LARC) patients with organ sparing treatment approaches, there is a growing interest in realising predictive models able to identify patients who will completely respond to nCRT before the start of therapy. These predictive models can be based on the analysis of clinical parameters, DNA sequences or radiomic parameters extracted by diagnostic images [12, 13]
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