Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience

Carlo N De Cecco; Maria Ciolina; Damiano Caruso; Marco Rengo; Balaji Ganeshan; Felix G Meinel; Daniela Musio; Francesca De Felice; Vincenzo Tombolini; Andrea Laghi

doi:10.1007/s00261-016-0733-8

Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience

Abdom Radiol (NY). 2016 Sep;41(9):1728-35. doi: 10.1007/s00261-016-0733-8.

Authors

Affiliations

¹ Diagnostic Imaging Unit, Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, University of Rome "Sapienza" - Polo Pontino, Latina, Italy.
² Department of Radiology, Medical University of South Carolina, Charleston, SC, USA.
³ Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Falmer, Sussex, United Kingdom.
⁴ Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital, Munich, Germany.
⁵ Department of Radiotherapy, University of Rome "Sapienza", Rome, Italy.
⁶ Diagnostic Imaging Unit, Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, University of Rome "Sapienza" - Polo Pontino, Latina, Italy. andrea.laghi@uniroma1.it.

PMID: 27056748
DOI: 10.1007/s00261-016-0733-8

Abstract

Purpose: To determine the performance of texture analysis (TA), diffusion-weighted imaging, and perfusion MR (pMRI) in predicting tumoral response in patients treated with neoadjuvant chemoradiotherapy (CRT).

Methods: 12 consecutive patients (8 females, 4 males, 63.2 ± 13.4 years) with rectal cancer were prospectively enrolled, and underwent pre-treatment 3T MRI. Treatment protocol consisted of neoadjuvant CRT with oxaliplatin and 5-fluorouracile. Unenhanced T2-weighted images TA (kurtosis), apparent diffusion coefficient (ADC), and pMRI parameters (Ktrans, Kep, Ve, IAUGC) were quantified by manually delineating a region of interest around the tumor outline. After CRT, all patients underwent complete surgical resection and the surgical specimen served as the gold standard. Receiver operating characteristic (ROC) curve analysis was performed to assess the discriminatory power of each quantitative parameter to predict complete response.

Results: Pathological complete response (pCR) was reported in six patients and partial response (PR) in three patients. Three patients were classified as non-responders (NR). Pre-treatment kurtosis was significantly lower in the pCR sub-group in comparison with PR + NR (p = .01). Among ADC and pMRI parameters, only Ve was significantly lower in the pCR sub-group compared with PR + NR (p = .01). A significant negative correlation between kurtosis and ADC (r = -0.650, p = .022) was observed. Pre-treatment area under the ROC curves (AUC), to discriminate between pCR and PR + NR, was significantly higher for kurtosis (0.861, p = .001) and Ve (0.861, p = .003) compared to all other parameters. The optimal cutoff value for pre-treatment kurtosis and Ve was ≤0.19 (100% sensitivity, 67% specificity) and ≤0.311 (83% sensitivity, 83% specificity), respectively.

Conclusion: Pre-treatment kurtosis derived from T2w images and Ve from pMRI have the potential to act as imaging biomarkers of rectal cancer response to neoadjuvant CRT.

Keywords: Diffusion-weighted imaging; Magnetic resonance imaging; Neoadjuvant chemoradiotherapy; Perfusion imaging; Rectal cancer; Texture analysis.

MeSH terms

Chemoradiotherapy
Diffusion Magnetic Resonance Imaging
Female
Humans
Male
Middle Aged
Neoadjuvant Therapy
Perfusion Imaging
Rectal Neoplasms*
Treatment Outcome