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Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development

  • Abdominal Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

Materials and methods

This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC).

Results

The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM.

Conclusion

Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.

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Data availability

Data are available on request due to restrictions, eg, privacy or ethical.

Abbreviations

3D-CRT:

Conformational radiotherapy techniques

5-FU:

5-Fluorouracil

ADC:

Apparent diffusion coefficient

AIC:

Akaike information criterion

cCR:

Clinical complete response

CI:

Confidence interval

CM:

Combined model

CR:

Complete response

CT:

Computed tomography

DFS:

Disease-free survival

DRE:

Digital rectal examination

DWI:

Diffusion-weighted imaging

GTV:

Gross tumor volume

IMRT:

Intensity-modulated radiation therapy

LARC:

Locally advanced rectal cancer

LC:

Local control

LE:

Local excision

MRF:

Mesorectal fascia

MRI:

Magnetic resonance imaging

MTB:

Multidisciplinary tumor board

nCRT:

Neoadjuvant chemoradiotherapy

ncCR:

Near complete response

NPV:

Negative predictive value

OS:

Overall survival

pCR:

Pathological complete response

rRM:

Radiomics model

ROC:

Receiver operating characteristic

ROI:

Region of interest

RT:

Radiotherapy

SIB:

Simultaneous integrated boost

TaMIS:

Transanal minimally invasive surgery

TEM:

Transanal endoscopic microsurgery

TME:

Total mesorectal excision

VMAT:

Volumetric modulated arc therapy

WW:

Watch-and-wait

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Authors and Affiliations

  1. Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy

    Giuditta Chiloiro, Davide Cusumano, Jacopo Lenkowicz, Luca Boldrini, Brunella Barbaro, Barbara Corvari, Nicola Dinapoli, Elisa Meldolesi, Riccardo Manfredi, Vincenzo Valentini & Maria Antonietta Gambacorta

  2. Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy

    Paola de Franco, Davide Carano, Brunella Barbaro, Martina Giraffa, Riccardo Manfredi, Vincenzo Valentini & Maria Antonietta Gambacorta

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  1. Giuditta Chiloiro
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Correspondence to Paola de Franco.

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The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Fondazione Policlinico Universitario Agostino Gemelli IRCCS (protocol code 3699, approved on December 22, 2020).

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Chiloiro, G., Cusumano, D., de Franco, P. et al. Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development. Radiol med 127, 11–20 (2022). https://doi.org/10.1007/s11547-021-01421-0

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