Search

PurposeThe aim of this study was to develop a novel approach for predicting the expression status of Epidermal Growth Factor Receptor (EGFR) and its subtypes in patients with Non-Small Cell Lung Cancer (NSCLC) using a Three-Dimensional Convolutional Neural Network (3D-CNN) ConvNeXt, radiomics features and clinical features.Materials and methodsA total of 732 NSCLC patients with available CT imaging and EGFR expression data were included in this retrospective study. The region of interest (ROI) was manually segmented, and clinicopathological features were collected. Radiomic and deep learning features were extracted. The instances were randomly divided into training, validation, and test sets. Feature selection was performed, and XGBoost was used to create solo models and combined models to predict the presence of EGFR and subtypes mutations. The effectiveness of the models was assessed using ROC and PRC curves.ResultsWe established the following models: ModelCNN, Modelradiomic, Modelclinical, ModelCNN+radiomic, ModelCNN+clinical, Modelradiomic+clinical, and ModelCNN+radiomic+clinical, which were based on deep learning features, radiomic features, clinical data and combinations of these, respectively. In predicting EGFR mutations, ModelCNN+radiomic+clinical demonstrated superior performance compared to other prediction models, achieving an AUC of 0.801. For distinguishing between EGFR subtypes ex19del and L858R, ModelCNN+radiomic reached the highest AUC value of 0.775.ConclusionsBoth deep learning models and radiomic signature-based models offer reasonably accurate non-invasive predictions of EGFR status and its subtypes. Fusion models hold the potential to enhance noninvasive methods for predicting EGFR mutations and subtypes, presenting a more reliable prediction approach.

PULSAR (personalized, ultra-fractionated stereotactic adaptive radiotherapy) is the adaptation of stereotactic ablative radiotherapy towards personalized cancer management. It has potential to harness the synergy between radiation therapy and immunotherapy, such as immune checkpoint inhibitors to amplify the anti-tumor immune response. For the first time, we applied a transformer-based attention mechanism to investigate the underlying interactions between combined PULSAR and PD-L1 blockade immunotherapy, based on the preliminary experimental results of a murine cancer model (Lewis Lung Carcinoma, LLC). The radiation and administration of α-PD-L1 were viewed as two external stimulation signals occurring in a temporal sequence. Our study demonstrates the utility of a transformer model in 1) predicting tumor changes in response to specific treatment schemes, and 2) generating self-attention and cross-attention maps. The cross-attention maps serve as a biological representation of the semantic similarity between source and target sentences in neural translation, offering insights into the causal relationships of the PULSAR effect. Our model offers a unique perspective with the potential to enhance the understanding of the temporal dependencies of the PULSAR effect on time, dose, and T cell dynamics. In a broader context, our proposed framework offers the potential to explore varying intervals and doses for subsequent treatments while monitoring the biological parameters impacted by these perturbations. This approach can lead to more personalized and rational radiation or drug interactions. [ABSTRACT FROM AUTHOR]

PurposeTo develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features.Method and materialsThis study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves.ResultsThe support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731–0.877) and an AUC=0.735 (95% CI 0.566–0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913–1.0) in the training cohort and an AUC=0.890 (95% CI 0.778–0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826–0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804–0.893) in the validation cohort.ConclusionOur findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.

BackgroundClinical benefits of neoadjuvant Anlotinib for locally advanced esophageal squamous cell carcinoma (ESCC) remains unclear. This study evaluated the efficacy and safety of neoadjuvant Anlotinib plus chemotherapy followed by minimally invasive esophagectomy (MIE) for the treatment of patients with locally advanced ESCC.MethodsPatients with locally advanced ESCC were randomly assigned to neoadjuvant Anlotinib combined with chemotherapy (Anlotinib group) or neoadjuvant chemoradiotherapy alone (nCRT group) with an allocation ratio of 1:1. The primary endpoint was the R0 surgical resection rate. Secondary endpoints included postoperative pathologic stage, complete response (CR) rate, and safety. Safety was assessed by adverse events (AEs) and postoperative complications.ResultsFrom August 2019 to August 2021, 93 patients were assigned to the nCRT or Anlotinib group. Of the 93 patients, 79 underwent MIE and were finally included in the per-protocol set (nCRT group: n=39; Anlotinib group: n=40). The R0 resection rate was 97.4% for nCRT versus 100.0% for Anlotinib group (p>0.05). Compared with the nCRT group, patients in the Anlotinib group had shorter total operation duration (262.2 ± 39.0 vs. 200.7 ± 25.5 min, p=0.010) and less blood loss (161.3 ± 126.7 vs. 52.4 ± 39.3 mL, p0.05). Besides, the incidences of AEs (80.0% vs. 92.3%) and postoperative complications (22.5% vs. 30.8%) were similar between the two groups (all p>0.05).ConclusionsNeoadjuvant Anlotinib plus chemotherapy had a similar safety profile and pathologic response, but better surgical outcomes than nCRT for locally advanced ESCC.

ObjectivesTo build and evaluate a deep learning radiomics nomogram (DLRN) for preoperative prediction of lung metastasis (LM) status in patients with soft tissue sarcoma (STS).MethodsIn total, 242 patients with STS (training set, n=116; external validation set, n=126) who underwent magnetic resonance imaging were retrospectively enrolled in this study. We identified independent predictors for LM-status and evaluated their performance. The minimum redundancy maximum relevance (mRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm were adopted to screen radiomics features. Logistic regression, decision tree, random forest, support vector machine (SVM), and adaptive boosting classifiers were compared for their ability to predict LM. To overcome the imbalanced distribution of the LM data, we retrained each machine-learning classifier using the synthetic minority over-sampling technique (SMOTE). A DLRN combining the independent clinical predictors with the best performing radiomics prediction signature (mRMR+LASSO+SVM+SMOTE) was established. Area under the receiver operating characteristics curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the performance and clinical applicability of the models.ResultComparisons of the AUC values applied to the external validation set revealed that the DLRN model (AUC=0.833) showed better prediction performance than the clinical model (AUC=0.664) and radiomics model (AUC=0.799). The calibration curves indicated good calibration efficiency and the DCA showed the DLRN model to have greater clinical applicability than the other two models.ConclusionThe DLRN was shown to be an accurate and efficient tool for LM-status prediction in STS.

ObjectiveTo establish a radiomics signature and a nomogram model based on enhanced CT images to predict the Ki-67 index of lung cancer.MethodsFrom January 2014 to December 2018, 282 patients with lung cancer who had undergone enhanced CT scans and Ki-67 examination within 2 weeks were retrospectively enrolled and analyzed. The clinical data of the patients were collected, such as age, sex, smoking history, maximum tumor diameter and serum tumor markers. Our primary cohort was randomly divided into a training group (n=197) and a validation group (n=85) at a 7:3 ratio. A Ki-67 index ≤ 40% indicated low expression, and a Ki-67 index > 40% indicated high expression. In total, 396 radiomics features were extracted using AK software. Feature reduction and selection were performed using the lasso regression model. Logistic regression analysis was used to establish a multivariate predictive model to identify high and low Ki-67 expression in lung cancer. A nomogram integrating the radiomics score was established based on multiple logistic regression analysis. Area under the curve (AUC) was used to evaluate the prediction efficiency of the radiomics signature and nomogram.ResultsThe AUC,sensitivity, specificity and accuracy of the radiomics signature in the training and validation groups were 0.88 (95% CI: 0.82~0.93),79.2%,84.3%,81.2% and 0.86 (95% CI: 0.78~0.94),74.6%,88.1%,79.8%, respectively. A nomogram combining radiomics features and clinical risk factors (smoking history and NSE) was developed. The AUC, sensitivity, specificity and accuracy were 0.87 (95% CI: 0.80~0.95), 75.0%, 90.2% and 83.5% in the validation group, respectively.ConclusionThe radiomics signature and nomogram based on enhanced CT images provide a way to predict the Ki-67 expression level in lung cancer.

ObjectivesTo investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors.MethodsData of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively.ResultsThe efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures.ConclusionsThe radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.

Background: Clinical benefits of neoadjuvant Anlotinib for locally advanced esophageal squamous cell carcinoma (ESCC) remains unclear. This study evaluated the efficacy and safety of neoadjuvant Anlotinib plus chemotherapy followed by minimally invasive esophagectomy (MIE) for the treatment of patients with locally advanced ESCC. Methods: Patients with locally advanced ESCC were randomly assigned to neoadjuvant Anlotinib combined with chemotherapy (Anlotinib group) or neoadjuvant chemoradiotherapy alone (nCRT group) with an allocation ratio of 1:1. The primary endpoint was the R0 surgical resection rate. Secondary endpoints included postoperative pathologic stage, complete response (CR) rate, and safety. Safety was assessed by adverse events (AEs) and postoperative complications. Results: From August 2019 to August 2021, 93 patients were assigned to the nCRT or Anlotinib group. Of the 93 patients, 79 underwent MIE and were finally included in the per-protocol set (nCRT group: n=39; Anlotinib group: n=40). The R0 resection rate was 97.4% for nCRT versus 100.0% for Anlotinib group (p>0.05). Compared with the nCRT group, patients in the Anlotinib group had shorter total operation duration (262.2 ± 39.0 vs. 200.7 ± 25.5 min, p=0.010) and less blood loss (161.3 ± 126.7 vs. 52.4 ± 39.3 mL, p<0.001). No significant differences were found in the postoperative pathologic stage between the Anlotinib group and nCRT group (all p>0.05). Besides, the incidences of AEs (80.0% vs. 92.3%) and postoperative complications (22.5% vs. 30.8%) were similar between the two groups (all p>0.05). Conclusions: Neoadjuvant Anlotinib plus chemotherapy had a similar safety profile and pathologic response, but better surgical outcomes than nCRT for locally advanced ESCC. [ABSTRACT FROM AUTHOR]