A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients.

OBJECTIVES: To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy. METHODS: LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed. RESULTS: Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018). CONCLUSION: Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT: Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS: • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.

Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks.

Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gains can be obtained if decisions are taken as early as possible as a function of the complexity of the input time series. The decision on when to stop inference and produce a decision must rely on an estimate of the current accuracy of the decision. Prior work demonstrated the use of conformal prediction (CP) as a principled way to quantify uncertainty and support adaptive-latency decisions in SNNs. In this paper, we propose to enhance the uncertainty quantification capabilities of SNNs by implementing ensemble models for the purpose of improving the reliability of stopping decisions. Intuitively, an ensemble of multiple models can decide when to stop more reliably by selecting times at which most models agree that the current accuracy level is sufficient. The proposed method relies on different forms of information pooling from ensemble models and offers theoretical reliability guarantees. We specifically show that variational inference-based ensembles with p-variable pooling significantly reduce the average latency of state-of-the-art methods while maintaining reliability guarantees.

Determination of the structures of molecularly imprinted polymers and xerogels using an automated stochastic approach.

An automated stochastic docking program with a graphical user interface, RANDOMDOCK (RD), has been developed to aid the development of molecularly imprinted polymers and xerogels. RD supports computations with ab initio and semiempirical quantum chemistry programs. The RD algorithms have been tested by searching for the most stable geometries of a varying number of methacrylic acid molecules interacting with nicotinamide. The optimal structures found are either as stable or more stable than those previously proposed for this molecularly imprinted polymer, illustrating that RD is capable of identifying the lowest-energy structures out of a potentially vast number of possible configurations. RD was subsequently applied to determine the most favorable binding sites between silane molecules and tetracycline (TC) as well as TC analogues. Hydrogen bonding between the templates and a silane is an important determinant of stability. Dispersion interactions are also sizable, sometimes dominant, especially between the largest silane and TC analogues not possessing a site readily available for hydrogen bonding. We highlight the importance of exploring the full intermolecular potential energy landscape when studying systems which may not afford highly specific interactions.

An overall survival predictive nomogram to identify high-risk patients among locoregionally advanced nasopharyngeal carcinoma: Developed based on the SEER database and validated institutionally.

Objective: Locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, even at the same stage, have different prognoses. We aim to construct a prognostic nomogram for predicting the overall survival (OS) to identify the high-risk LA-NPC patients. Materials and methods: Histologically diagnosed WHO type II and type III LA-NPC patients in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled as the training cohort (n= 421), and LA-NPC patients from Shantou University Medical College Cancer Hospital (SUMCCH) served as the external validation cohort (n= 763). Variables were determined in the training cohort through Cox regression to form a prognostic OS nomogram, which was verified in the validation cohort, and compared with traditional clinical staging using the concordance index (C-index), Kaplan-Meier curves, calibration curves and decision curve analysis (DCA). Patients with scores higher than the specific cut-off value determined by the nomogram were defined as high-risk patients. Subgroup analyses and high-risk group determinants were explored. Results: Our nomogram had a higher C-index than the traditional clinical staging method (0.67 vs. 0.60, p<0.001). Good agreement between the nomogram-predicted and actual survival were shown in the calibration curves and DCA, indicating a clinical benefit of the nomogram. High-risk patients identified by our nomogram had worse prognosis than the other groups, with a 5-year overall survival (OS) of 60.4%. Elderly patients at advanced stage and without chemotherapy had a tendency for high risk than the other patients. Conclusions: Our OS predictive nomogram for LA-NPC patients is reliable to identify high-risk patients.

Impact of dyslipidemia on the severity of symptomatic lumbar spine degeneration: A retrospective clinical study.

Background: Lumbar intervertebral disc degeneration (IVDD) is an important cause of low back pain or sciatica, and metabolic factors play an important role. However, little is known about the relationship of dyslipidemia to the risk of intervertebral disc degeneration (IVDD). This study aimed to assess the impact of serum lipid levels on the severity of lumbar disc degeneration and to investigate its association with endplate inflammation. Methods: We conducted a case retrospective study in which a total of 302 hospitalized Chinese patients were recruited, of whom 188 (112 males and 76 females; mean age: 51.66 years) were without underlying disease, while the remaining 114 patients (51 males and 63 females; mean age: 62.75 years) had underlying diseases. We examined fasting serum lipid levels for total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Magnetic resonance imaging (MRI) was used to determine endplate inflammation. Pfirrmann grading and Weishaupt grading were used to evaluate the severity of intervertebral disc degeneration and facet joint degeneration, respectively. Results: There was no difference in age, gender, and general BMI between the two groups (P > 0.05), but there were significantly high levels in TC, LDL-C, and LDL-C/HDL-C (P = 0.04, P = 0.013, P = 0.01, respectively). TG and HDL-C showed no significant difference (P = 0.064, P = 0.336, respectively). The multivariate logistic regression model showed that age was a risk factor for the occurrence of endplate inflammation. In the group without underlying diseases, age, but not other indicators, was a risk factor for the occurrence of endplate inflammation (P < 0.01), In the group with underlying diseases, none of the patient indicators was directly related to the occurrence of endplate inflammation (P > 0.05). A nonlinear machine learning model was used to measure the contribution of each factor to the disease outcome and to analyze the effect between the top three contributing factors and the outcome variables. In patients without underlying diseases, the top three factors contributing to the severity grading of intervertebral disc degeneration were age (32.9%), high-density lipoproteins (20.7%), and triglycerides (11.8%). For the severity grading of facet joint degeneration, the top three contributing factors were age (27.7%), high-density lipoproteins (19.4%), and triglycerides (14.6%). For patients with underlying diseases, the top three factors contributing to intervertebral disc degeneration were age (25.4%), BMI (15.3%), and low-density lipoprotein/high-density lipoprotein ratio (13.9%). In terms of degree classification for facet joint degeneration, the top three contributing factors were age (17.5%), BMI (17.2%), and total cholesterol (16.7%). Conclusion: This study shows that age, high-density lipoprotein, and triglycerides affect the degree of degeneration in patients with symptomatic lumbar degeneration without underlying diseases. Age and BMI are two major factors affecting the severity of degeneration in patients with underlying diseases, and dyslipidemia is a secondary factor. However, there is no clear association between dyslipidemia and the occurrence of endplate inflammation in either group.