OBJECTIVES: To investigate the potential of DCE-MRI to discriminate responders from non-responders after neoadjuvant chemo-radiotherapy (CRT) for locally advanced rectal cancer (LARC). We investigated several shape parameters for the time-intensity curve (TIC) in order to identify the best combination of parameters between two linear parameter classifiers. METHODS: Seventy-four consecutive patients with LARC were enrolled in a prospective study approved by our ethics committee. Each patient gave written informed consent. After surgery, pathological TNM and tumour regression grade (TRG) were estimated. DCE-MRI semi-quantitative analysis (sqMRI) was performed to identify the best parameter or parameter combination to discriminate responders from non-responders in response monitoring to CRT. Percentage changes of TIC shape descriptors from the baseline to the presurgical scan were assessed and correlated with TRG. Receiver operating characteristic analysis and linear classifier were applied. RESULTS: Forty-six patients (62.2%) were classified as responders, while 28 subjects (37.8%) were considered as non-responders. sqMRI reached a sensitivity of 93.5% and a specificity of 82.1% combining the percentage change in Maximum Signal Difference (ΔMSD) and Wash-out Slope (ΔWOS), the Standardized Index of Shape (SIS). CONCLUSIONS: SIS obtains the best result in discriminating responders from non-responders after CRT in LARC, with a cut-off value of -3.0%. KEY POINTS: • DCE-MRI shape descriptors are investigated to assess preoperative CRT response in LARC. • Identification of the best TIC shape descriptors combination through a linear classifier. • Identification of a single MRI index to predict neoadjuvant treatment response.
OBJECTIVES: To investigate the potential of DCE-MRI to discriminate responders from non-responders after neoadjuvant chemo-radiotherapy (CRT) for locally advanced rectal cancer (LARC). We investigated several shape parameters for the time-intensity curve (TIC) in order to identify the best combination of parameters between two linear parameter classifiers. METHODS: Seventy-four consecutive patients with LARC were enrolled in a prospective study approved by our ethics committee. Each patient gave written informed consent. After surgery, pathological TNM and tumour regression grade (TRG) were estimated. DCE-MRI semi-quantitative analysis (sqMRI) was performed to identify the best parameter or parameter combination to discriminate responders from non-responders in response monitoring to CRT. Percentage changes of TIC shape descriptors from the baseline to the presurgical scan were assessed and correlated with TRG. Receiver operating characteristic analysis and linear classifier were applied. RESULTS: Forty-six patients (62.2%) were classified as responders, while 28 subjects (37.8%) were considered as non-responders. sqMRI reached a sensitivity of 93.5% and a specificity of 82.1% combining the percentage change in Maximum Signal Difference (ΔMSD) and Wash-out Slope (ΔWOS), the Standardized Index of Shape (SIS). CONCLUSIONS:SIS obtains the best result in discriminating responders from non-responders after CRT in LARC, with a cut-off value of -3.0%. KEY POINTS: • DCE-MRI shape descriptors are investigated to assess preoperative CRT response in LARC. • Identification of the best TIC shape descriptors combination through a linear classifier. • Identification of a single MRI index to predict neoadjuvant treatment response.
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