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OBJECTIVES: To evaluate amide proton transfer-weighted (APTw)-derived whole-tumor histogram analysis parameters in predicting pathological extramural venous invasion (pEMVI) positive status of rectal adenocarcinoma (RA). METHODS: Preoperative MR including APTw imaging of 125 patients with RA (mean 61.4 ± 11.6 years) were retrospectively analyzed. Two radiologists reviewed each case's EMVI status based on the MR-based modified 5-point scale system with conventional MR images. The APTw histogram parameters of primary tumors were obtained automatically using whole-tumor volume histogram analysis. The independent risk factors markedly correlated with pEMVI-positive status were assessed using univariate and multivariate logistic regression analyses. Diagnosis performance was assessed by receiver operating characteristic curve (ROC) analysis. The AUCs were compared using the Delong method. RESULTS: Univariate analysis demonstrated that MR-tumor (T) stage, MR-lymph node (N) stage, APTw-10%, APTw-90%, interquartile range, APTw-minimum, APTw-maximum, APTw-mean, APTw-median, entropy, kurtosis, mean absolute deviation (MAD), and robust MAD were significantly related to pEMVI-positive status (all p < 0.05). Multivariate analysis demonstrated that MR-T stage (OR = 4.864, p = 0.018), MR-N stage (OR = 4.967, p = 0.029), interquartile range (OR = 0.892, p = 0.037), APT-minimum (OR = 1.046, p = 0.031), entropy (OR = 11.604, p = 0.006), and kurtosis (OR = 1.505, p = 0.007) were the independent risk factors enabling prediction of pEMVI-positive status. The AUCs for diagnostic ability of conventional MRI assessment, the APTw histogram model, and the combined model (including APTw histogram and clinical variables) were 0.785, 0.853, and 0.918, respectively. The combined model outperformed the APTw histogram model (p = 0.013) and the conventional MRI assessment (p = 0.006). CONCLUSIONS: Whole-tumor histogram analysis of APTw images combined with clinical factors showed better diagnosis efficiency in predicting EMVI involvement in RA. KEY POINTS: ⢠Rectal adenocarcinomas with pEMVI-positive status are typically associated with higher APTw-SI values. ⢠APTw-minimum, interquartile range, entropy, kurtosis, MR-T stage, and MR-N stage are the independent risk factors for EMVI involvement. ⢠The best prediction for EMVI involvement was obtained with a combined model of APTw histogram and clinical variables (area under the curve, 0.918).
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Adenocarcinoma , Neoplasias del Recto , Humanos , Protones , Amidas , Carga Tumoral , Estudios Retrospectivos , Imagen por Resonancia Magnética/métodos , Neoplasias del Recto/diagnóstico por imagen , Neoplasias del Recto/patología , Adenocarcinoma/diagnóstico por imagen , Adenocarcinoma/patologíaRESUMEN
OBJECTIVES: The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers' discriminative performance for pCR prediction. METHODS: This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models. CONCLUSIONS: The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients. KEY POINTS: ⢠A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. ⢠The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. ⢠The RF classifier performed best in the current study.
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Neoplasias del Recto , Humanos , Estudios Retrospectivos , Neoplasias del Recto/diagnóstico por imagen , Neoplasias del Recto/terapia , Neoplasias del Recto/patología , Imagen por Resonancia Magnética , Teorema de Bayes , Recto/patologíaRESUMEN
OBJECTIVES: To construct and validate multiparametric MR-based radiomic models based on primary tumors for predicting lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients. METHODS: A total of 150 LARC patients from two independent centers were enrolled. The training cohort comprised 100 patients from center A. Fifty patients from center B were included in the external validation cohort. Radiomic features were extracted from the manually segmented volume of interests of the primary tumor before and after nCRT. Feature selection was performed using multivariate logistic regression analysis. The clinical risk factors were selected via the least absolute shrinkage and selection operator method. The radiologist's assessment of LNM was performed. Eight models were constructed using random forest classifiers, including four single-sequence models, three combined-sequence models, and a clinical model. The models' discriminative performance was assessed via receiver operating characteristic curve analysis quantified by the area under the curve (AUC). RESULTS: The AUCs of the radiologist's assessment, the clinical model, and the single-sequence models ranged from 0.556 to 0.756 in the external validation cohort. Among the single-sequence models, modelpost_DWI exhibited superior predictive power, with an AUC of 0.756 in the external validation set. In combined-sequence models, modelpre_T2_DWI_post had the best diagnostic performance in predicting LNM after nCRT, with a significantly higher AUC (0.831) than those of the clinical model, modelpre_T2_DWI, and the single-sequence models (all p < 0.05). CONCLUSIONS: A multiparametric model that incorporates MR radiomic features before and after nCRT is optimal for predicting LNM after nCRT in LARC. CRITICAL RELEVANCE STATEMENT: This study enrolled 150 LARC patients from two independent centers and constructed multiparametric MR-based radiomic models based on primary tumors for predicting LNM following nCRT, which aims to guide therapeutic decisions and predict prognosis for LARC patients. KEY POINTS: The biological characteristics of primary tumors and metastatic LNs are similar in rectal cancer. Radiomics features and clinical data before and after nCRT provide complementary tumor information. Preoperative prediction of LN status after nCRT contributes to clinical decision-making.
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OBJECTIVES: Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively. METHODS: A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis. RESULTS: The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively. CONCLUSION: The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.
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Adenocarcinoma , Neoplasias del Recto , Humanos , Metástasis Linfática/diagnóstico por imagen , Protones , Estudios Retrospectivos , Teorema de Bayes , Imagen por Resonancia Magnética/métodos , Neoplasias del Recto/patología , Adenocarcinoma/diagnóstico por imagen , Adenocarcinoma/secundarioRESUMEN
OBJECTIVE: The aim of this study is to investigate the diagnostic ability of diffusion kurtosis imaging (DKI) -derived parameters combining with clinical data as risk factors for EMVI's involvement status in rectal adenocarcinoma. MATERIALS AND METHODS: Preoperative MR examination including DKI and conventional diffusion-weighted imaging (DWI) was performed on 154 rectal adenocarcinoma patients enrolled in this respective study. Kmean, Dmean, and apparent diffusion coefficient (ADC) values were calculated. Clinical information, serum tumor markers, MR and pathological assessment of EMVI were recorded. The Shapiro-Wilk test, two-sample t-test, Mann-Whitney U test, Spearman's rank-order correlation, univariate and multivariate logistic regression analyses were used for statistical analysis. Receiver operating characteristic (ROC) curve analyses were performed to identify risk factors in EMVI involvement. RESULTS: Of the 154 patients, pEMVI-positive rectal tumors had significantly higher Kmean values, lower ADCmean values compared to pEMVI-negative rectal tumors. Kmean values positively correlated with mrEMVI scores, whereas ADCmean values showed a negative correlation with mrEMVI scores. However, there was no significant correlation between the Dmean values and the mrEMVI scores. Univariate analysis demonstrated increased Kmean values, decreased ADCmean values, nodal involvement, an advanced tumor stage, and a G2 tumor grade were significantly related to the pEMVI of rectal adenocarcinoma. The multivariate analysis demonstrated that the Kmean values, lymph node involvement and an advanced tumor stage (T3) were independent risk factors for EMVI. CONCLUSION: The potential for diffusion kurtosis imaging as a biomarker for evaluating the EMVI of rectal cancer is feasible, especially given DKI's capability of detecting tumor heterogeneity noninvasively.
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Adenocarcinoma , Neoplasias del Recto , Adenocarcinoma/diagnóstico por imagen , Adenocarcinoma/patología , Imagen de Difusión por Resonancia Magnética/métodos , Imagen de Difusión Tensora , Humanos , Curva ROC , Neoplasias del Recto/diagnóstico por imagen , Neoplasias del Recto/patologíaRESUMEN
OBJECTIVE: To evaluate amide proton weighted (APTw) MRI combined with diffusion-weighted imaging (DWI) in predicting neoadjuvant chemoradiotherapy (NCRT) response in patients with locally advanced rectal cancer (LARC). METHODS: 53 patients with LARC were enrolled in this retrospective study. MR examination including APTw MRI and DWI was performed before and after NCRT. APTw SI, ADC value, tumor size, CEA level before and after NCRT were assessed. The difference of the above parameters between before and after NCRT was calculated. The tumor regression grading (TRG) was assessed by American Joint Committee on Cancer's Cancer Staging Manual AJCC 8th score. The Shapiro-Wilk test, paired t-test and Wilcoxon Signed Ranks test, two-sample t-test, Mann-Whitney U test and multivariate analysis were used for statistical analysis. RESULTS: Of the 53 patients, 19 had good responses (TRG 0-1), 34 had poor responses (TRG 2-3). After NCRT, all the rectal tumors demonstrated decreased APT values, increased ADC values, reduced tumor volumes and CEA levels (all p < 0.001). Good responders demonstrated higher pre-APT values, higher Δ APT values, lower pre- ADC values and higher Δ tumor volumes than poor responders. Pre-APT combined with pre-ADC achieved the best diagnostic performance, with AUC of 0.895 (sensitivity of 85.29%, specificity of 89.47%, p < 0.001) in predicting good response to NCRT. CONCLUSION: The combination of APTw and DWI may serve as a noninvasive biomarker for evaluating and identifying response to NCRT in LARC patients.