Prediction of the efficacy of neoadjuvant chemoradiotherapy in patients with rectal cancer based on a texture analysis of T2-weighted magnetic resonance tumor image obtained at primary staging

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Abstract

BACKGROUND: Recently, significant efforts have been undertaken to find potential noninvasive biomarkers for predicting the response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy.

AIM: To assess the texture characteristics of locally advanced rectal cancer in primary T2-weighted imaging (T2-WI) as a potential predictor for the efficacy of standard neoadjuvant chemoradiotherapy and develop a prediction system for the efficacy of neoadjuvant chemoradiotherapy based on them.

MATERIALS AND METHODS: The retrospective study enrolled 82 patients with locally advanced rectal cancer who received combination treatment with neoadjuvant chemoradiotherapy. Patient data were divided into the training (n=58) and control (n=24) sets. For texture analysis, primary high-resolution T2-WI at the level of the tumor center, oriented perpendicular to the intestinal wall, was used. The texture analysis was performed by second-order statistics based on the gray-level co-occurrence matrices using MAZDA ver. 4.6 featuring the calculation of 11 texture parameters. In the training set, based on the morphological assessment of surgical specimens, significantly different texture analysis parameters were found for two groups of patients: neoadjuvant chemoradiotherapy responders (good prognosis group) and nonresponders (poor prognosis group). Accordingly, a scoring system was created for assessing the efficacy of neoadjuvant chemoradiotherapy. The system was tested on the control set, and diagnostic efficacy parameters were determined.

RESULTS: In the training set, the good and poor prognosis groups differed significantly in five texture parameters: AngScMom (p=0.021), SumofSqs (p=0.003), SumEntrp (p=0.003), Entropy (p=0.038), and SumVarnc (p=0.015), for which the cutoff points were found. These parameters were applied to create the scoring system (excluding the Entropy parameter, which had a strong direct correlation with SumEntrp and the lowest area under the curve, and SumofSqs, which had low reproducibility). The diagnostic efficiency of the scoring system for predicting the response had sensitivity, specificity, positive-predictive value, and negative- predictive value of 72%, 69%, 70%, and 71% for the training set and 80%, 64%, 62%, and 82% for the control set, respectively. The areas under the ROC curve were 0.77 and 0.72 for the training and control sets, respectively.

CONCLUSIONS: Texture analysis of the primary T2-WI of tumors in patients with locally advanced rectal cancer allows for predicting the efficacy of neoadjuvant chemoradiotherapy with moderate diagnostic efficiency. The results suggest good prospects for further research in this area.

About the authors

Yana A. Dayneko

A.F. Tsyb Medical Radiology Research Centre, National Medical Research Radiological Center

Author for correspondence.
Email: vorobeyana@gmail.com
ORCID iD: 0000-0002-4524-0839
SPIN-code: 1841-7759

MD, Cand. Sci. (Medicine)

Russian Federation, Obninsk

Tatiana P. Berezovskaya

A.F. Tsyb Medical Radiology Research Centre, National Medical Research Radiological Center

Email: tberezovska@yahoo.com
ORCID iD: 0000-0002-3549-4499
SPIN-code: 5837-3465

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Obninsk

Oleg A. Mirzeabasov

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: oami@yandex.ru
ORCID iD: 0000-0001-5587-2795
SPIN-code: 3820-4320

MD, Assistant Professor

Russian Federation, Obninsk

Sergey O. Starkov

National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)

Email: sergeystarkov56@mail.ru
ORCID iD: 0000-0002-0420-7856

Dr. Sci. (Physical and Mathematical), Professor

Russian Federation, Obninsk

Sofiya A. Myalina

A.F. Tsyb Medical Radiology Research Centre, National Medical Research Radiological Center

Email: samyalina@mail.ru
ORCID iD: 0000-0001-6686-5419
SPIN-code: 9668-3834
Russian Federation, Obninsk

Aleksey A. Nevolskikh

A.F. Tsyb Medical Radiology Research Centre, National Medical Research Radiological Center

Email: editor@omnidoctor.ru
ORCID iD: 0000-0001-5961-2958
SPIN-code: 3787-6139

MD, Dr. Sci. (Medicine)

Russian Federation, Obninsk

Sergey А. Ivanov

A.F. Tsyb Medical Radiology Research Centre, National Medical Research Radiological Center; Peoples’ Friendship University of Russia

Email: oncourolog@gmail.com
ORCID iD: 0000-0001-7689-6032
SPIN-code: 4264-5167

MD, Dr. Sci. (Medicine), Professor, corresponding member of the Russian Academy of Sciences

Russian Federation, Obninsk; Moscow

Andrey D. Kaprin

Peoples’ Friendship University of Russia; P.A. Herzen Moscow Research Institute of Oncology, National Medical Research Radiological Center; National Medical Research Radiological Centre

Email: contact@nmicr.ru
ORCID iD: 0000-0001-8784-8415
SPIN-code: 1759-8101

MD, Dr. Sci. (Medicine), Professor, academician of the Russian Academy of Sciences

Russian Federation, Moscow; Moscow; Moscow

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2. Fig. 1. Magnetic resonance imaging of the pelvis, T2-weighted image, oblique-axial section, tumor of the lower ampullar part of the rectum: a ― before neoadjuvant chemoradiotherapy; b ― after segmentation (the area for automatic calculation of texture parameters is highlighted in white).

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3. Fig. 2. Distribution of quantitative values ​​of texture analysis parameters of locally advanced rectal cancer in groups of patients with good and poor prognosis for neoadjuvant chemoradiotherapy in the training set.

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4. Fig. 3. Correlation between texture analysis parameters in the training sample. The orange gradation indicates the strength of the direct correlation, the blue gradation indicates the strength of the inverse correlation (a more intense color corresponds to a greater strength of the correlation).

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5. Fig. 4. ROC curves for texture analysis parameters in the training set: a ― for AngScMom; b ― for Entropy, SumEntrp, SumofSqs, SumVarnc.

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6. Fig. 5. ROC curve for the scoring system for predicting the response to neoadjuvant chemoradiotherapy based on T2-WI texture analysis in the training set, AUC=0.76.

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7. Fig. 6. ROC curve for the scoring system for predicting tumor response to neoadjuvant chemoradiotherapy based on texture analysis of the initial T2-WI in the control sample, AUC=0.72.

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