T-staging of rectal cancer: accuracy of 3.0 Tesla MRI compared with 1.5 Tesla

OBJECTIVES: Magnetic resonance imaging (MRI) is not accurate in discriminating T1-2 from borderline T3 rectal tumors. Higher resolution on 3 Tesla-(3T)-MRI could improve diagnostic performance for T-staging. The aim of this study was to determine whether 3T-MRI compared with 1.5 Tesla-(1.5T)-MRI improves the accuracy for the discrimination between T1-2 and borderline T3 rectal tumors and to evaluate reproducibility. METHODS: 13 patients with non-locally advanced rectal cancer underwent imaging with both 1.5T and 3T-MRI. Three readers with different expertise evaluated the images and predicted T-stage with a confidence level score. Receiver operator characteristics curves with areas under the curve (AUC) and diagnostic parameters were calculated. Inter- and intra-observer agreements were calculated with quadratic kappa-weighting. Histology was the reference standard. RESULTS: Seven patients had pT1-2 tumors and six had pT3 tumors. AUCs ranged from 0.66 to 0.87 at 1.5T vs. 0.52-0.82 at 3T. Mean overstaging rate was 43% at 1.5T and 57% at 3T (P = 0.23). Inter-observer agreement was kappa 0.50-0.71 at 1.5T vs. 0.15-0.68 at 3T. Intra-observer agreement was kappa 0.71 at 1.5T and 0.76 at 3T. CONCLUSIONS: This is the first study to compare 3T with 1.5T MRI for T-staging of rectal cancer within the same patients. Our results showed no difference between 3T and 1.5T-MRI for the distinction between T1-2 and borderline T3 tumors, regardless of expertise. The higher resolution at 3T-MRI did not aid in the distinction between desmoplasia in T1-2-tumors and tumor stranding in T3-tumors. Larger studies are needed to acknowledge these findings

Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer

Funding Information: This study has received funding from the Dutch Cancer Society (project number 10138). Publisher Copyright: © 2023, The Author(s).Objectives: To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset. Methods: Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1–2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97). Results: After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48–0.72) to predict complete response and 0.65 (95%CI=0.53–0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables. Conclusions: Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset). Clinical relevance statement: Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization. Key Points: This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.Peer reviewe