Purpose: To evaluate volumetric magnetic resonance (MR) imaging for predicting recurrence-free survival (RFS) after neoadjuvant chemotherapy (NACT) of breast cancer and to consider its predictive performance relative to pathologic complete response (PCR).
Materials And Methods: This HIPAA-compliant prospective multicenter study was approved by institutional review boards with written informed consent. Women with breast tumors 3 cm or larger scheduled for NACT underwent dynamic contrast-enhanced MR imaging before treatment (examination 1), after one cycle (examination 2), midtherapy (examination 3), and before surgery (examination 4). Functional tumor volume (FTV), computed from MR images by using enhancement thresholds, and change from baseline (ΔFTV) were measured after one cycle and before surgery. Association of RFS with FTV was assessed by Cox regression and compared with association of RFS with PCR and residual cancer burden (RCB), while controlling for age, race, and hormone receptor (HR)/ human epidermal growth factor receptor type 2 (HER2) status. Predictive performance of models was evaluated by C statistics.
Results: Female patients (n = 162) with FTV and RFS were included. At univariate analysis, FTV2, FTV4, and ΔFTV4 had significant association with RFS, as did HR/HER2 status and RCB class. PCR approached significance at univariate analysis and was not significant at multivariate analysis. At univariate analysis, FTV2 and RCB class had the strongest predictive performance (C statistic = 0.67; 95% confidence interval [CI]: 0.58, 0.76), greater than for FTV4 (0.64; 95% CI: 0.53, 0.74) and PCR (0.57; 95% CI: 0.39, 0.74). At multivariate analysis, a model with FTV2, ΔFTV2, RCB class, HR/HER2 status, age, and race had the highest C statistic (0.72; 95% CI: 0.60, 0.84).
Conclusion: Breast tumor FTV measured by MR imaging is a strong predictor of RFS, even in the presence of PCR and RCB class. Models combining MR imaging, histopathology, and breast cancer subtype demonstrated the strongest predictive performance in this study.
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http://dx.doi.org/10.1148/radiol.2015150013 | DOI Listing |
BMC Chem
January 2025
Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333 031, India.
A large set of antimalarial molecules (N ~ 15k) was employed from ChEMBL to build a robust random forest (RF) model for the prediction of antiplasmodial activity. Rather than depending on high throughput screening (HTS) data, molecules tested at multiple doses against blood stages of Plasmodium falciparum were used for model development. The open-access and code-free KNIME platform was used to develop a workflow to train the model on 80% of data (N ~ 12k).
View Article and Find Full Text PDFEur J Med Res
January 2025
Department of Otolaryngology, Affiliated Hospital of Hebei University, 212th Yuhua Road, Baoding, Hebei, China.
The patient's body temperature significantly fluctuates, affected by factors, including anesthesia. The ideal temperature monitoring method that is suitable for perioperative application is of great significance for identifying hypothermia and malignant hyperthermia early, as well as for guiding intraoperative temperature protection. This study aims to compare the cutaneous zero-heat-flux (ZHF) thermometer application in general anesthesia using the infrared tympanic measurement as a reference.
View Article and Find Full Text PDFBioData Min
January 2025
Department of Statistics, College of Science, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia.
Background: This study employs a LSTM-FC neural networks to address the critical public health issue of child undernutrition in Ethiopia. By employing this method, the study aims classify children's nutritional status and predict transitions between different undernutrition states over time. This analysis is based on longitudinal data extracted from the Young Lives cohort study, which tracked 1,997 Ethiopian children across five survey rounds conducted from 2002 to 2016.
View Article and Find Full Text PDFCancer Imaging
January 2025
Department of Radiology, Chongqing University Cancer Hospital, Chongqing, China.
Background: Previous studies utilizing dual-energy CT (DECT) for evaluating treatment efficacy in nasopharyngeal cancinoma (NPC) are limited. This study aimed to investigate whether the parameters from DECT can predict the response to induction chemotherapy in NPC patients in two centers.
Methods: This two-center retrospective study included patients diagnosed with NPC who underwent contrast-enhanced DECT between March 2019 and November 2023.
BMC Oral Health
January 2025
Department of Anaesthesiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Background: Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer.
Methods: A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated.
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