Development of a simultaneous quantification method of imatinib and sunitinib and their main metabolites and its application in patients with gastrointestinal stromal tumor.

Correlations between plasma concentrations of imatinib and sunitinib with efficacy and toxicity have been established. It is crucial to develop a sensitive and precise method for determining the plasma concentrations of imatinib and sunitinib, along with their active metabolites, to facilitate therapeutic drug monitoring and individualized therapy. Plasma samples were separated on an Agilent ZORBAX SB-C18 chromatographic column using gradient elution. Quantification was performed using a mass spectrometer equipped with electrospray ionization in multiple reaction monitoring. The analysis time was 18 min per run, with all analytes and internal standards eluting within 8 min. The calibration range was 25-4000 ng/mL for imatinib, 5-800 ng/mL for N-desmethyl imatinib (CGP74588), and 2.5-400 ng/mL for sunitinib and N-desethyl sunitinib (SU12662). Intra- and inter-assay precision were both below 15%, and accuracy ranged between 90.0% and 101.9%. The method was successfully applied to determine blood samples from 120 patients with gastrointestinal stromal tumors who received imatinib (n = 115) and sunitinib (n = 5). It has been validated as linear, accurate, precise, and robust, making it suitable for therapeutic drug monitoring of imatinib and sunitinib in routine clinical practice.

Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage.

It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method.

Machine learning for predicting breast-conserving surgery candidates after neoadjuvant chemotherapy based on DCE-MRI.

Purpose: This study aimed to investigate a machine learning method for predicting breast-conserving surgery (BCS) candidates, from patients who received neoadjuvant chemotherapy (NAC) by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) obtained before and after NAC. Materials and methods: This retrospective study included 75 patients who underwent NAC and breast surgery. First, 3,390 features were comprehensively extracted from pre- and post-NAC DCE-MRIs. Then patients were then divided into two groups: type 1, patients with pathologic complete response (pCR) and single lesion shrinkage; type 2, major residual lesion with satellite foci, multifocal residual, stable disease (SD), and progressive disease (PD). The logistic regression (LR) was used to build prediction models to identify the two groups. Prediction performance was assessed using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Radiomics features were significantly related to breast cancer shrinkage after NAC. The combination model achieved an AUC of 0.82, and the pre-NAC model was 0.64, the post-NAC model was 0.70, and the pre-post-NAC model was 0.80. In the combination model, 15 features, including nine wavelet-based features, four Laplacian-of-Gauss (LoG) features, and two original features, were filtered. Among these selected were four features from pre-NAC DCE-MRI, six were from post-NAC DCE-MRI, and five were from pre-post-NAC features. Conclusion: The model combined with pre- and post-NAC DCE-MRI can effectively predict candidates to undergo BCS and provide AI-based decision support for clinicians with ensured safety. High-order (LoG- and wavelet-based) features play an important role in our machine learning model. The features from pre-post-NAC DCE-MRI had better predictive performance.

Association between preoperative skeletal muscle mass depletion and poor relapse-free survival in patients with gastrointestinal stromal tumors after complete resection.

OBJECTIVES: The aim of this study was to investigate the predictive effects of skeletal muscle mass (SMM) depletion on relapse risk in patients who had undergone complete surgical resection for primary resectable gastrointestinal stromal tumors (GISTs). METHODS: This retrospective study comprised 445 enrolled patients with primary resectable GISTs who had undergone surgical treatment between January 2013 and January 2021. The lumbar skeletal muscle index (SMI) was assessed using abdominal computed tomography images taken within 7 d preoperatively. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors for nomogram construction. Predictive accuracy and discriminative ability were measured using the concordance index (C-index). RESULTS: Three- and 5-y relapse-free survival (RFS) rates for patients in the low SMI group were significantly worse than those in the high SMI group (81.3 and 75.4% versus 92.3 and 91.6%, respectively; P < 0.001). In stratification analysis using modified National Institutes of Health criteria, high-risk patients with low SMI showed significantly shorter RFS (P = 0.001). Multivariate analysis indicated that tumor size, tumor location, mitotic rates, the platelet-to-lymphocyte ratio, the prognostic nutritional index, and SMM depletion were independent prognostic factors for RFS (P < 0.05). These six variables were selected for nomogram construction, which showed superior discrimination with a C-index of 0.82. CONCLUSIONS: There was a significant association between preoperative SMM depletion and a high risk for relapse in patients who had undergone complete resection for primary resectable GISTs, especially in patients with high-risk GIST. Our simple, practical, novel nomogram intuitively predicted RFS in these patients.