MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer.

The aim of this study is to evaluate the predictive ability of MRI-based radiomics combined with tumor markers for TN staging in patients with rectal cancer and to develop a prediction model for TN staging. A total of 190 patients with rectal adenocarcinoma who underwent total mesorectal excision at the First Affiliated Hospital of the Air Force Medical University between January 2016 and December 2020 were included in the study. An additional 54 patients from a prospective validation cohort were included between August 2022 and August 2023. Preoperative tumor markers and MRI imaging data were collected from all enrolled patients. The 190 patients were divided into a training cohort (n = 133) and a validation cohort (n = 57). Radiomics features were extracted by outlining the region of interest (ROI) on T2WI sequence images. Feature selection and radiomics score (Rad-score) construction were performed using least absolute shrinkage and selection operator regression analysis (LASSO). The postoperative pathology TNM stage was used to differentiate locally advanced rectal cancer (T3/4 or N1/2) from locally early rectal cancer (T1/2, N0). Logistic regression was used to construct separate prediction models for T stage and N stage. The models' predictive performance was evaluated using DCA curves and calibration curves. The T staging model showed that Rad-score, based on 8 radiomics features, was an independent predictor of T staging. When combined with CEA, tumor diameter, mesoretal fascia (MRF), and extramural venous invasion (EMVI), it effectively differentiated between T1/2 and T3/4 stage rectal cancers in the training cohort (AUC 0.87 [95% CI: 0.81-0.93]). The N-staging model found that Rad-score, based on 10 radiomics features, was an independent predictor of N-staging. When combined with CA19.9, degree of differentiation, and EMVI, it effectively differentiated between N0 and N1/2 stage rectal cancers. The training cohort had an AUC of 0.84 (95% CI: 0.77-0.91). The calibration curves demonstrated good precision between the predicted and actual results. The DCA curves indicated that both sets of predictive models could provide net clinical benefits for diagnosis. MRI-based radiomics features are independent predictors of T staging and N staging. When combined with tumor markers, they have good predictive efficacy for TN staging of rectal cancer.

Analysis of Clinical characteristics to predict pathologic complete response for patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy.

To explore clinical characteristics which could be applied to predict pathologic complete response (pCR) for patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy (neo-CRT) and total mesorectal excision (TME). 297 patients with locally advanced rectal cancer (cT3-4 or cN+) who were treated with neo-CRT followed by TME were retrospectively reviewed. Clinical characteristics including age, gender, tumor distance from anus, serum CEA, hemoglobin levels before treatment and clinical TN stage were used to investigate the association with pCR after neo-CRT. Seventy-nine (26.6%) patients achieved pCR after neo-CRT. pCR were achieved in 42 (34.4%) patients in cT1-3 stage and 37 (21.1%) in cT4 stage. pCR rate was 36.4% and 16.4% for patients with pre-treatment serum CEA ≤5.33ng/ml and >5.33ng/ml, respectively. Uni- and multi-variate analyses revealed that pre-treatment serum CEA level ≤5.33ng/ml and clinical T stage, (i.e., cT1-3 versus cT4) were highly correlated with pCR (p < 0.05). Clinical T stage and pre-treatment serum CEA level were strongly associated with pCR for patients with locally advanced rectal cancer treated with neo-CRT followed by TME which could be applied as clinical predictors for pCR.