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Diagnostic performance of MRI for assessment of response to neoadjuvant chemoradiotherapy in oesophageal cancer.

ABSTRACT: BACKGROUND:Patients with a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for oesophageal cancer may benefit from non-surgical management. The aim of this study was to determine the diagnostic performance of visual response assessment of the primary tumour after nCRT on T2-weighted (T2W) and diffusion-weighted (DW) MRI. METHODS:Patients with locally advanced oesophageal cancer who underwent T2W- and DW-MRI (1·5?T) before and after nCRT in two hospitals, between July 2013 and September 2017, were included in this prospective study. Three radiologists evaluated T2W images retrospectively using a five-point score for the assessment of residual tumour in a blinded manner and immediately rescored after adding DW-MRI. Histopathology of the resection specimen was used as the reference standard; ypT0 represented a pCR. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC) and interobserver agreement were calculated. RESULTS:Twelve of 51 patients (24 per cent) had a pCR. The sensitivity and specificity of T2W-MRI for detection of residual tumour ranged from 90 to 100 and 8 to 25 per cent respectively. Respective values for T2W?+?DW-MRI were 90-97 and 42-50 per cent. AUCs for the three readers were 0·65, 0·66 and 0·68 on T2W-MRI, and 0·71, 0·70 and 0·70 on T2W?+?DW-MRI (P?=?0·441, P?=?0·611 and P?=?0·828 for readers 1, 2 and 3 respectively). The ? value for interobserver agreement improved from 0·24-0·55 on T2W-MRI to 0·55-0·71 with DW-MRI. CONCLUSION:Preoperative assessment of residual tumour on MRI after nCRT for oesophageal cancer is feasible with high sensitivity, reflecting a low chance of missing residual tumour. However, the specificity was low; this results in overstaging of complete responders as having residual tumour and, consequently, overtreatment.

SUBMITTER: Vollenbrock SE

PROVIDER: S-EPMC6594024 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Project description:Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy has been proposed as an alternative treatment approach for cervical cancer (CC) in stage Ib2-IIb, who had a strong desire to be treated with surgery. Our study aims to develop a model based on multimodal MRI by using radiomics and deep learning to predict the treatment response in CC patients treated with neoadjuvant chemoradiotherapy (NACRT). From August 2009 to June 2013, CC patients in stage Ib2-IIb (FIGO 2008) who received NACRT at Fujian Cancer Hospital were enrolled in our study. Clinical information, contrast-enhanced T1-weighted imaging (CE-T1WI), and T2-weighted imaging (T2WI) data were respectively collected. Radiomic features and deep abstract features were extracted from the images using radiomics and deep learning models, respectively. Then, ElasticNet and SVM-RFE were employed for feature selection to construct four single-sequence feature sets. Early fusion of two multi-sequence feature sets and one hybrid feature set were performed, followed by classification prediction using four machine learning classifiers. Subsequently, the performance of the models in predicting the response to NACRT was evaluated by separating patients into training and validation sets. Additionally, overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan-Meier survival curves. Among the four machine learning models, SVM exhibited the best predictive performance (AUC=0.86). Among the seven feature sets, the hybrid feature set achieved the highest values for AUC (0.86), ACC (0.75), Recall (0.75), Precision (0.81), and F1-score (0.75) in the validation set, outperforming other feature sets. Furthermore, the predicted outcomes of the model were closely associated with patient OS and DFS (p = 0.0044; p = 0.0039). A model based on MRI images with features from multiple sequences and different methods could precisely predict the response to NACRT in CC patients. This model could assist clinicians in devising personalized treatment plans and predicting patient survival outcomes.

Project description:BackgroundExcessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour-intensive. In this study, we built machine-learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results.MethodsWe randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine-learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ2 method, 10.0% decrease of skeletal muscle index over 50 days was determined as the cut-off value to define the excessive skeletal muscle loss.ResultsThe five input variables were significantly different between the excessive and the non-excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%-89.5%) and 74.5% (95% CI: 62.7%-86.3%), respectively.ConclusionsMachine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.

Project description:ObjectivesA watch and wait strategy with the goal of organ preservation is an emerging treatment paradigm for rectal cancer following neoadjuvant treatment. However, the selection of appropriate patients remains a challenge. Most previous efforts to measure the accuracy of MRI in assessing rectal cancer response used a small number of radiologists and did not report variability among them.MethodsTwelve radiologists from 8 institutions assessed baseline and restaging MRI scans of 39 patients. The participating radiologists were asked to assess MRI features and to categorize the overall response as complete or incomplete. The reference standard was pathological complete response or a sustained clinical response for > 2 years.ResultsWe measured the accuracy and described the interobserver variability of interpretation of rectal cancer response between radiologists at different medical centers. Overall accuracy was 64%, with a sensitivity of 65% for detecting complete response and specificity of 63% for detecting residual tumor. Interpretation of the overall response was more accurate than the interpretation of any individual feature. Variability of interpretation was dependent on the patient and imaging feature investigated. In general, variability and accuracy were inversely correlated.ConclusionsMRI-based evaluation of response at restaging is insufficiently accurate and has substantial variability of interpretation. Although some patients' response to neoadjuvant treatment on MRI may be easily recognizable, as seen by high accuracy and low variability, that is not the case for most patients.Key points• The overall accuracy of MRI-based response assessment is low and radiologists differed in their interpretation of key imaging features. • Some patients' scans were interpreted with high accuracy and low variability, suggesting that these patients' pattern of response is easier to interpret. • The most accurate assessments were those of the overall response, which took into consideration both T2W and DWI sequences and the assessment of both the primary tumor and the lymph nodes.

Project description:BackgroundThis study aimed to develop a model that automatically predicts the neoadjuvant chemoradiotherapy (nCRT) response for patients with locally advanced cervical cancer (LACC) based on T2-weighted MR images and clinical parameters.MethodsA total of 138 patients were enrolled, and T2-weighted MR images and clinical information of the patients before treatment were collected. Clinical information included age, stage, pathological type, squamous cell carcinoma (SCC) level, and lymph node status. A hybrid model extracted the domain-specific features from the computational radiomics system, the abstract features from the deep learning network, and the clinical parameters. Then, it employed an ensemble learning classifier weighted by logistic regression (LR) classifier, support vector machine (SVM) classifier, K-Nearest Neighbor (KNN) classifier, and Bayesian classifier to predict the pathologic complete response (pCR). The area under the receiver operating characteristics curve (AUC), accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and precision were used as evaluation metrics.ResultsAmong the 138 LACC patients, 74 were in the pCR group, and 64 were in the non-pCR group. There was no significant difference between the two cohorts in terms of tumor diameter (p = 0.787), lymph node (p = 0.068), and stage before radiotherapy (p = 0.846), respectively. The 109-dimension domain features and 1472-dimension abstract features from MRI images were used to form a hybrid model. The average AUC, ACC, TPR, TNR, and precision of the proposed hybrid model were about 0.80, 0.71, 0.75, 0.66, and 0.71, while the AUC values of using clinical parameters, domain-specific features, and abstract features alone were 0.61, 0.67 and 0.76, respectively. The AUC value of the model without an ensemble learning classifier was 0.76.ConclusionsThe proposed hybrid model can predict the radiotherapy response of patients with LACC, which might help radiation oncologists create personalized treatment plans for patients.

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Diagnostic performance of MRI for assessment of response to neoadjuvant chemoradiotherapy in oesophageal cancer.

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