What is the predictive value of pretreatment MRI characteristics for achieving a complete response after total neoadjuvant treatment in locally advanced rectal cancer?

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Tác giả: Hande Ozen Atalay, Emre Balik, Dursun Bugra, Rohat Esmer, Bengi Gurses, Zeynep Unal Kabaoglu, Duygu Karahacioglu, Ibrahim Halil Ozata, Ahmet Rencuzogullari, Burcu Saka, Fatih Selcukbiricik, Ugur Selek, Sukran Senyurek, Orhun Çig Taskin

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: Ireland : European journal of radiology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 702486

 OBJECTIVES: To investigate the value of pretreatment magnetic resonance imaging (MRI) features in predicting a complete response to total neoadjuvant treatment (TNT) in locally advanced rectal cancer (LARC). METHODS: The data of patients who received TNT were analyzed retrospectively. MRI features, including T stage, morphology, length, and volume
  the presence of MR-detected extramural venous invasion (mrEMVI), the number of mrEMVI, and the diameter of the largest invaded vein
  main vein mrEMVI
  presence of MR-detected tumor deposits (mrTDs), the number of mrTDs, and the size of the largest mrTD
  MR-detected lymph node status (mrLN)
  tumor distance from the anal verge
  mesorectal fascia involvement (mrMRF + )
  and mean apparent diffusion coefficient (ADC) values were recorded. Patients were classified as complete (CRs) or noncomplete responders (non-CRs) according to the pathological/clinical outcomes. For patients managed nonoperatively, a sustained clinical complete response for >
  2 years was deemed a surrogate endpoint for complete response. The MRI parameters were categorized into three distinct groups: baseline, advanced, and quantitative features, and were analyzed using multivariable stepwise logistic regression. The ability to predict complete response was evaluated by comparing different combinations of MRI parameters, and performance on an "independent" dataset was estimated using bootstrapped leave-one-out cross-validation (LOOCV). RESULTS: The data of 84 patients were evaluated (CRs, n = 44
  non-CRs, n = 40). The optimal model, which included baseline and quantitative MRI features, achieved an area under the curve of 0.837 for predicting complete response. Selected predictors were T stage and ADC mean value. Advanced MRI features did not improve the performance of the model. CONCLUSION: A multivariable model combining T stage and the ADC mean value can help identify LARC patients who are likely to a achieve complete response before the initiation of TNT.
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