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Performance and Dimensionality of Pretreatment MRI Radiomics in Rectal Carcinoma Chemoradiotherapy Prediction

Journal of Clinical Medicine, ISSN: 2077-0383, Vol: 13, Issue: 2
2024
  • 1
    Citations
  • 0
    Usage
  • 5
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    5
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Research on Rectal Cancer Reported by a Researcher at Institute for Oncology and Radiology of Serbia (Performance and Dimensionality of Pretreatment MRI Radiomics in Rectal Carcinoma Chemoradiotherapy Prediction)

2024 JAN 30 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Daily News -- Research findings on rectal cancer are discussed in

Article Description

(1) Background: This study aimed to develop a machine learning model based on radiomics of pretreatment magnetic resonance imaging (MRI) 3D T2W contrast sequence scans combined with clinical parameters (CP) to predict neoadjuvant chemoradiotherapy (nCRT) response in patients with locally advanced rectal carcinoma (LARC). The study also assessed the impact of radiomics dimensionality on predictive performance. (2) Methods: Seventy-five patients were prospectively enrolled with clinicopathologically confirmed LARC and nCRT before surgery. Tumor properties were assessed by calculating 2141 radiomics features. Least absolute shrinkage selection operator (LASSO) and multivariate regression were used for feature selection. (3) Results: Two predictive models were constructed, one starting from 72 CP and 107 radiomics features, and the other from 72 CP and 1862 radiomics features. The models revealed moderately advantageous impact of increased dimensionality, with their predictive respective AUCs of 0.86 and 0.90 in the entire cohort and 0.84 within validation folds. Both models outperformed the CP-only model (AUC = 0.80) which served as the benchmark for predictive performance without radiomics. (4) Conclusions: Predictive models developed in this study combining pretreatment MRI radiomics and clinicopathological features may potentially provide a routine clinical predictor of chemoradiotherapy responders, enabling clinicians to personalize treatment strategies for rectal carcinoma.

Bibliographic Details

Marinkovic, Mladen; Stojanovic-Rundic, Suzana; Stanojevic, Aleksandra; Tomasevic, Aleksandar; Jankovic, Radmila; Zoidakis, Jerome; Castellví-Bel, Sergi; Fijneman, Remond J A; Cavic, Milena; Radulovic, Marko

MDPI AG

Medicine

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