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Abstract Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moiré-twisted bilayer magnets of CrI3, we showcase the method’s high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems.

Objectives Brain metastases (BMs) are commonly categorized into cystic and solid. However, the difference in the prognosis of patients with either cystic or solid BMs following radiotherapy remains poorly understood. We used a retrospective design to elucidate the disparities in survival between these two patient groups undergoing radiotherapy and to identify factors influencing the overall survival (OS) of patients with BMs. Methods This retrospective study encompasses 212 patients diagnosed with BMs. We meticulously analyzed the clinical characteristics, radiation therapy modalities, and risk factors influencing the OS among these patients, categorized by BMs type, post-brain radiation therapy. Results A statistically significant difference in mOS was observed between the two cohorts (Solid vs Cystic: 23.1 vs 14.6 months). Subgroup analysis unveiled distinctions in mOS, particularly in patients with EGFR-mutant lung adenocarcinoma (Solid vs Cystic: 23.1 vs 6.43 months). The volume of BMs and the biological effective dose (BED) emerged as significantly prognostic factors for patients with cystic BMs. For patients with solid BMs, fraction dose, BED, and the number of BMs were identified as independent prognostic factors for survival. Conclusion Brain radiotherapy shows superior survival benefits for lung cancer patients with solid BMs compared to those with cystic BMs, particularly in EGFR-mutant lung cancer. In particular, patients receiving BED ≥60 Gy have a more favorable prognosis than those receiving BED

Objectives: To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC). Methods: A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness. Results: In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction. Conclusion: CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC.

IntroductionYoung cervical cancer patients who require ovarian transposition usually have their ovaries moved away from the pelvic radiotherapy (RT) field before radiotherapy. The dose of ovaries during radiotherapy is closely related to the location of the ovaries. To protect ovarian function and avoid ovarian dose exceeding the limits, a safe location of transposed ovary must be determined prior to surgery.MethodsFor this purpose, we input the patient's preoperative CT into a neural network model to predict the dose distribution. Surgeons were able to quickly locate low-dose regions based on the dose distribution before surgery, thus determining the safe location of the transposed ovary. In this work, we proposed a new progressive refinement transformer model PRT-Net that can generate dose prediction at multiple scale resolutions in one forward propagation, and refine the dose prediction using prediction details from low to high resolution based on a deep supervision strategy. A multi-loss function fusion algorithm was also built to fit the prediction results under different loss dimensions. The clinical feasibility of the method was verified through an actual cases.Results and discussionTherefore, using PRT-Net to predict the dose distribution by preoperative CT in cervical cancer patients can assist clinicians to perform ovarian transposition surgery and prevent patients' ovaries from exceeding the prescribed dose limit in postoperative radiotherapy.