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Clinical parameters model for predicting pathologic complete response following preoperative chemoradiation in patients with esophageal cancer.

ABSTRACT: Approximately 25% of patients with esophageal cancer (EC) who undergo preoperative chemoradiation, achieve a pathologic complete response (pathCR). We hypothesized that a model based on clinical parameters could predict pathCR with a high (?60%) probability.We analyzed 322 patients with EC who underwent preoperative chemoradiation. All the patients had baseline and postchemoradiation positron emission tomography (PET) and pre- and postchemoradiation endoscopic biopsy. Logistic regression models were used for analysis, and cross-validation via the bootstrap method was carried out to test the model.The 70 (21.7%) patients who achieved a pathCR lived longer (median overall survival [OS], 79.76 months) than the 252 patients who did not achieve a pathCR (median OS, 39.73 months; OS, P = 0.004; disease-free survival, P = 0.003). In a logistic regression analysis, the following parameters contributed to the prediction model: postchemoradiation PET, postchemoradiation biopsy, sex, histologic tumor grade, and baseline (EUS)T stage. The area under the receiver-operating characteristic curve was 0.72 (95% confidence interval [CI] 0.662-0.787); after the bootstrap validation with 200 repetitions, the bias-corrected AU-ROC was 0.70 (95% CI 0.643-0.728).Our data suggest that the logistic regression model can predict pathCR with a high probability. This clinical model could complement others (biomarkers) to predict pathCR.

SUBMITTER: Ajani JA

PROVIDER: S-EPMC3457750 | biostudies-other | 2012 Oct

REPOSITORIES: biostudies-other

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Clinical parameters model for predicting pathologic complete response following preoperative chemoradiation in patients with esophageal cancer.

Ajani J A JA Correa A M AM Hofstetter W L WL Rice D C DC Blum M A MA Suzuki A A Taketa T T Welsh J J Lin S H SH Lee J H JH Bhutani M S MS Ross W A WA Maru D M DM Macapinlac H A HA Erasmus J J Komaki R R Mehran R J RJ Vaporciyan A A AA Swisher S G SG

Annals of oncology : official journal of the European Society for Medical Oncology 20120724 10

<h4>Background</h4>Approximately 25% of patients with esophageal cancer (EC) who undergo preoperative chemoradiation, achieve a pathologic complete response (pathCR). We hypothesized that a model based on clinical parameters could predict pathCR with a high (≥60%) probability.<h4>Patients and methods</h4>We analyzed 322 patients with EC who underwent preoperative chemoradiation. All the patients had baseline and postchemoradiation positron emission tomography (PET) and pre- and postchemoradiatio ...[more]

PMID: 22831985

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Project description:PURPOSE:To examine the value of early changes in quantitative diffusion-weighted imaging and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for discriminating complete pathologic response (pCR) to chemoradiation in esophageal cancer. METHODS AND MATERIALS:Twenty esophageal cancer patients treated with chemoradiation followed by surgery were prospectively enrolled. Patients underwent magnetic resonance imaging and FDG-PET/CT scans at baseline, interim (2 weeks after chemoradiation start), and first follow-up. On the basis of pathologic findings at surgery, patients were categorized into tumor regression groups (TRG1, TRG2, and TRG3+). Distributions of summary statistics in apparent diffusion coefficient (ADC) and FDG-PET at baseline and relative changes at interim and follow-up scans were compared between pCR/TRG1 and non-pCR/TRG2+ groups and across readers. Receiver operating characteristics were evaluated for summary measures to characterize discrimination of pCR from non-pCR. RESULTS:Relative changes in tumor volume ADC (?ADC) mean and 25th and 10th percentiles from baseline to interim were able to completely discriminate (area under the curve = 1, P < .0011) between pCR and non-pCR (thresholds = 27.7%, 29.2%, and 32.1%, respectively) and were found to have high interreader reliability (95% limits of agreement of 1.001, 0.944, and 0.940, respectively). Relative change in total lesion glycolysis (TLG) from baseline to interim was significantly different among pCR and non-pCR groups (P=.0117) and yielded an area under the curve of 0.947 (95% confidence interval 0.8505-1.043). An optimal threshold of 59% decrease in TLG provided optimal sensitivity (specificity) of 1.000 (0.867). Changes in ADC summary measures were negatively correlated with that of TLG (Spearman, -0.495, P=.027). CONCLUSIONS:Quantitative volume ?ADC and TLG during treatment may serve as early imaging biomarkers for discriminating pathologic response to chemoradiation in esophageal cancer. Validation of these data in larger, prospective, multicenter studies is essential.

Project description:PurposePathologic complete response (pCR) is a critical factor in determining whether patients with rectal cancer (RC) should have surgery after neoadjuvant chemoradiotherapy (nCRT). Currently, a pathologist's histological analysis of surgical specimens is necessary for a reliable assessment of pCR. Machine learning (ML) algorithms have the potential to be a non-invasive way for identifying appropriate candidates for non-operative therapy. However, these ML models' interpretability remains challenging. We propose using explainable boosting machine (EBM) to predict the pCR of RC patients following nCRT.MethodsA total of 296 features were extracted, including clinical parameters (CPs), dose-volume histogram (DVH) parameters from gross tumor volume (GTV) and organs-at-risk, and radiomics (R) and dosiomics (D) features from GTV. R and D features were subcategorized into shape (S), first-order (L1), second-order (L2), and higher-order (L3) local texture features. Multi-view analysis was employed to determine the best set of input feature categories. Boruta was used to select all-relevant features for each input dataset. ML models were trained on 180 cases from our institution, with 37 cases from RTOG 0822 clinical trial serving as the independent dataset for model validation. The performance of EBM in predicting pCR on the test dataset was evaluated using ROC AUC and compared with that of three state-of-the-art black-box models: extreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM). The predictions of all black-box models were interpreted using Shapley additive explanations.ResultsThe best input feature categories were CP+DVH+S+R_L1+R_L2 for all models, from which Boruta-selected features enabled the EBM, XGB, RF, and SVM models to attain the AUCs of 0.820, 0.828, 0.828, and 0.774, respectively. Although EBM did not achieve the best performance, it provided the best capability for identifying critical turning points in response scores at distinct feature values, revealing that the bladder with maximum dose >50 Gy, and the tumor with maximum2DDiameterColumn >80 mm, elongation <0.55, leastAxisLength >50 mm and lower variance of CT intensities were associated with unfavorable outcomes.ConclusionsEBM has the potential to enhance the physician's ability to evaluate an ML-based prediction of pCR and has implications for selecting patients for a "watchful waiting" strategy to RC therapy.

Project description:PurposeTo develop a clinical-radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases.Materials and methodsThe MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical-radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC).ResultsThe clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical-radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR.ConclusionWe developed a clinical-radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.

Project description:ImportanceFor patients with locally advanced esophageal squamous cell carcinoma, neoadjuvant chemoradiation has been shown to improve long-term outcomes, but the treatment response varies among patients. Accurate pretreatment prediction of response remains an urgent need.ObjectiveTo determine whether peritumoral radiomics features derived from baseline computed tomography images could provide valuable information about neoadjuvant chemoradiation response and enhance the ability of intratumoral radiomics to estimate pathological complete response.Design, setting, and participantsA total of 231 patients with esophageal squamous cell carcinoma, who underwent baseline contrast-enhanced computed tomography and received neoadjuvant chemoradiation followed by surgery at 2 institutions in China, were consecutively included. This diagnostic study used single-institution data between April 2007 and December 2018 to extract radiomics features from intratumoral and peritumoral regions and established intratumoral, peritumoral, and combined radiomics models using different classifiers. External validation was conducted using independent data collected from another hospital during the same period. Radiogenomics analysis using gene expression profile was done in a subgroup of the training set for pathophysiological explanation. Data were analyzed from June to December 2019.ExposuresComputed tomography-based radiomics.Main outcomes and measuresThe discriminative performances of radiomics models were measured by area under the receiver operating characteristic curve.ResultsAmong the 231 patients included (192 men [83.1%]; mean [SD] age, 59.8 [8.7] years), the optimal intratumoral and peritumoral radiomics models yielded similar areas under the receiver operating characteristic curve of 0.730 (95% CI, 0.609-0.850) and 0.734 (0.613-0.854), respectively. The combined model was composed of 7 intratumoral and 6 peritumoral features and achieved better discriminative performance, with an area under the receiver operating characteristic curve of 0.852 (95% CI, 0.753-0.951), accuracy of 84.3%, sensitivity of 90.3%, and specificity of 79.5% in the test set. Gene sets associated with the combined model mainly involved lymphocyte-mediated immunity. The association of peritumoral area with response identification might be partially attributed to type I interferon-related biological process.Conclusions and relevanceA combination of peritumoral radiomics features appears to improve the predictive performance of intratumoral radiomics to estimate pathological complete response after neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. This study underlines the significant application of peritumoral radiomics to assess treatment response in clinical practice.

Dataset Information

Clinical parameters model for predicting pathologic complete response following preoperative chemoradiation in patients with esophageal cancer.

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Clinical parameters model for predicting pathologic complete response following preoperative chemoradiation in patients with esophageal cancer.

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