Predicting Complications in Head and Neck Surgery: Comparing Calculators to Surgeons.

Objectives: Surgical outcomes determine national ranking, reputation, and funding, and are often assessed with objective surgical risk calculators (SRCs). Surgeons' assessments are not considered. This study aims to determine if surgeons or SRCs are more accurate in predicting outcomes. Methods: This prospective cohort study identified a surgeon's assessment on a patient's risk preoperatively. The patient's risk was also calculated using the SRC. Predictions were compared to patient outcomes and to each other to assess whether surgeons or the SRC were more accurate. Results: Of the 101 patients included, 37 (36.6%) experienced a complication of any kind and 18 (17.8%) experienced a serious complication. Smoking resulted in a 2.49 times higher overall complication rate (P = .04). Laryngectomy patients experienced the highest rate of complications (P = .02) compared to those undergoing free flap reconstruction [odds ratio (OR) 0.9] or any other surgery (OR 0.26). Both surgeons and the American College of Surgeons (ACS) tool performed poorly on the prediction of the outcome of any complication, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.51 [95% confidence interval (CI): 0.39-0.62] and 0.58 (95% CI: 0.47-0.70), respectively, which was not statistically significant (P = .34). For the prediction of the outcome of serious complication, the AUC for surgeons and the ACS tool were 0.55 (95% CI: 0.41-0.69) and 0.60 (95% CI: 0.46-0.74), respectively, which was not statistically significant (P = .58). Conclusions: Neither validated risk calculators nor surgeons are accurate in predicting perioperative risk. The only risk factor that contributes to improving predictions for complications is preoperative smoking, although age and type of surgery are also significant predictors. Risk calculators may therefore not be appropriate metrics for assessing hospital performance. These findings can help guide preoperative counseling and may help in the development of more accurate predictive tools as the healthcare field continues to incorporate artificial intelligence into surgical planning.

Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.

BACKGROUND: The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status. PURPOSE: To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer. MATERIAL AND METHODS: We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared. RESULTS: The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model. CONCLUSION: The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches.

Pre-operative EQ-5D-5L is a strong predictor of meaningful improvement in quality of life following primary total knee arthroplasty.

INTRODUCTION: Predicting which patients will get meaningful benefit from total knee arthroplasty remains a challenge. Our aim was to assess if pre-operative quality of life (EuroQol 5-Dimension, 5-Level instrument; EQ-5D-5L) can predict the likelihood of a patient achieving post-operative improvement in patient-reported outcome measures (PROMS) following total knee arthroplasty to a level of minimum clinically-important difference (MCID). MATERIALS AND METHODS: This was a retrospective analysis of a prospective cohort of total knee arthroplasty patients. EQ-5D-5L and Oxford Knee Scores (OKS) were recorded pre-operatively, 6 months and 2 years post-operatively. The primary outcome measure was achievement of MCID in EQ-5D-5L at 2 years. Multivariable analysis through multiple logistic regression was performed to assess for independent predictors of MCID in EQ-5D-5L, OKS and re-operation at 2 years. RESULTS: 400 patients were included, with 57% female and a mean age of 66 years. Pre-operative EQ-5D-5L was the only strong predictor of post-operative EQ-5D-5L MCID (OR: 0.016, CI: 0.004 to 0.06), when adjusted for age, gender, BMI, ASA, smoking status and surgeon grade. The optimal pre-operative EQ-5D-5L threshold was found to be 0.53 by Youden's index, with a sensitivity of 70% and specificity of 73%. CONCLUSIONS: Pre-operative quality of life as measured by EQ-5D-5L is a strong independent predictor of reaching MCID in EQ-5D-5L following total knee arthroplasty. Those with worse EQ-5D-5L are more likely to gain meaningful benefit from knee arthroplasty.

Predicting resistance and pseudoprogression: are minimalistic immunoediting mathematical models capable of forecasting checkpoint inhibitor treatment outcomes in lung cancer?

BACKGROUND: The increased application of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 in lung cancer treatment generates clinical need to reliably predict individual patients' treatment outcomes. METHODS: To bridge the prediction gap, we examine four different mathematical models in the form of ordinary differential equations, including a novel delayed response model. We rigorously evaluate their individual and combined predictive capabilities with regard to the patients' progressive disease (PD) status through equal weighting of model-derived outcome probabilities. RESULTS: Fitting the complete treatment course, the novel delayed response model (R2=0.938) outperformed the simplest model (R2=0.865). The model combination was able to reliably predict patient PD outcome with an overall accuracy of 77% (sensitivity = 70%, specificity = 81%), solely through calibration with primary tumor longest diameter measurements. It autonomously identified a subset of 51% of patients where predictions with an overall accuracy of 81% (sensitivity = 81%, specificity = 81%) can be achieved. All models significantly outperformed a fully data-driven machine learning-based approach. IMPLICATIONS: These modeling approaches provide a dynamic baseline framework to support clinicians in treatment decisions by identifying different treatment outcome trajectories with already clinically available measurement data. LIMITATIONS AND FUTURE DIRECTIONS: Conjoint application of the presented approach with other predictive tools and biomarkers, as well as further disease information (e.g. metastatic stage), could further enhance treatment outcome prediction. We believe the simple model formulations allow widespread adoption of the developed models to other cancer types. Similar models can easily be formulated for other treatment modalities.

Evaluating the efficacy of immunotherapy in gastric cancer: Insights from immune checkpoint inhibitors.

The emergence of immunotherapy, particularly immune checkpoint inhibitors (ICIs), represents a groundbreaking approach to treating gastric cancer (GC). However, the prognosis of GC patients receiving ICI treatment is influenced by various factors. This manuscript identified sarcopenia and myosteatosis as inde-pendent prognostic factors impacting the outcomes of GC patients treated with ICIs. Additionally, this study introduced a visual predictive model to estimate the prognosis of GC patients. If confirmed by further studies, this observation could provide valuable insights to propel the advancement of personalized clinical medicine and the integration of precision medicine practices.

Development and Assessment of a Predictive Model for Ki-67 Expression Using Ultrasound Indicators and Non-Morphological Magnetic Resonance Imaging Parameters Before Breast Cancer Therapy.

To formulate a predictive model for assessing Ki-67 expression in breast cancer by integrating pre-treatment ultrasound features with non-morphological magnetic resonance imaging (MRI) parameters, encompassing functional and hemodynamic indicators. A retrospective study was conducted on 167 patients. All patients underwent a breast mass biopsy for histopathological and Ki-67 analysis prior to neoadjuvant chemotherapy (NAC) treatment. Additionally, all patients underwent ultrasonography and MRI examinations prior to the biopsy. The recorded variables were Ki-67, apparent diffusion coefficient (ADC) values, Max Slope, time to peak (TTP), signal enhancement ratio (SER), early enhancement rate (EER), time-signal intensity curve (TIC), tumor maximum diameter, tumor margins and boundaries, aspect ratio, microcalcification, color Doppler flow imaging grading, resistance index (RI), and axillary lymph node metastasis. Statistical analysis was performed using the R software package. Normally distributed continuous data are presented as mean ± standard deviation (SD), skewed continuous data as median, and categorical variables as frequency or percentage. The dataset was randomly divided into a modeling group and a validation group following a 7:3 ratio, employing a predetermined random seed. The selection of variables was conducted using the random forest algorithm. Specifically, in the initial analysis, we trained a random forest model using all available variables. By evaluating the Gini importance scores of each variable, we identified those that contributed the most to predicting Ki-67 expression. The predictive model for Ki-67 expression was constructed using selected variables: Maximum Diameter, ADC value, SER value, Max Slope value, TTP value, and EER value. Within the validation group, the evaluation metrics demonstrated an Area under the curve of 0.961 with a 95% confidence interval ranging from 0.865 to 0.995. The model achieved a kappa score of 1.00, precision of 0.949, recall of 1, an F1 score of 0.974, sensitivity of 100%, specificity of 85.71%, a positive predictive value of 94.87%, and a negative predictive value of 100%. The combination of non-morphological MRI parameters and pre-treatment ultrasound features in a breast cancer prediction model powered by RF machine learning demonstrated favorable clinical outcomes and improved diagnostic performance.

STAS: New explorations and challenges for thoracic surgeons.

Spread through air spaces (STAS) represents a relatively novel concept in the pathology of lung cancer, and it specifically refers to the dissemination of tumour cells into the parenchymal air spaces adjacent to the primary tumour. In 2015, the World Health Organization (WHO) classified STAS as a new invasive form of lung adenocarcinoma (LUAD). Many studies investigated the role of STAS and revealed its association with the prognosis of LUAD and its influence on the outcomes of other malignant pulmonary neoplasms. Additionally, the underlying mechanisms and predictive models of STAS have received considerable attention in recent years. This paper provides a comprehensive overview of the research advancements and prospects of STAS by examining it from multiple perspectives.

Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study.

BACKGROUND: A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening. OBJECTIVE: This study aims to realize the LHS vision by designing an equitable, machine learning (ML)-enabled LHS unit for LC screening. It focuses on developing an inclusive and practical LC risk prediction model, suitable for initializing the ML-enabled LHS (ML-LHS) unit. This model aims to empower primary physicians in a clinical research network, linking central hospitals and rural clinics, to routinely deliver risk-based screening for enhancing LC early detection in broader populations. METHODS: We created a standardized data set of health factors from 1397 patients with LC and 1448 control patients, all aged 30 years and older, including both smokers and nonsmokers, from a hospital's electronic medical record system. Initially, a data-centric ML approach was used to create inclusive ML models for risk prediction from all available health factors. Subsequently, a quantitative distribution of LC health factors was used in feature engineering to refine the models into a more practical model with fewer variables. RESULTS: The initial inclusive 250-variable XGBoost model for LC risk prediction achieved performance metrics of 0.86 recall, 0.90 precision, and 0.89 accuracy. Post feature refinement, a practical 29-variable XGBoost model was developed, displaying performance metrics of 0.80 recall, 0.82 precision, and 0.82 accuracy. This model met the criteria for initializing the ML-LHS unit for risk-based, inclusive LC screening within clinical research networks. CONCLUSIONS: This study designed an innovative ML-LHS unit for a clinical research network, aiming to sustainably provide inclusive LC screening to all at-risk populations. It developed an inclusive and practical XGBoost model from hospital electronic medical record data, capable of initializing such an ML-LHS unit for community and rural clinics. The anticipated deployment of this ML-LHS unit is expected to significantly improve LC-screening rates and early detection among broader populations, including those typically overlooked by existing screening guidelines.

A modified GLIM criteria-based nomogram for the survival prediction of gastric cancer patients undergoing surgical resection.

BACKGROUND: This study aimed to develop a comprehensive model based on five GLIM variables to predict the individual survival and provide more appropriate patient counseling. METHODS: This retrospective cohort study included 301 gastric cancer (GC) patients undergoing radical resection. C-reactive protein (CRP) as an inflammatory marker was included in GLIM criteria and a nomogram for predicting 5-year overall survival (OS) in GC patients was established. The Bootstrap repeated sampling for 1000 times was used for internal validation. RESULTS: Of the total 301 patients, 20 (6.64%) died within 5 years. CRP improved the sensitivity and accuracy of the survival prediction model (AUC = 0.782, 0.694 to 0.869 for the model without CRP; AUC = 0.880, 0.809 to 0.950 for the model adding CRP). Besides, a GLIM-based nomogram was established with an AUC of 0.889. The C-index for predicting OS was 0.878 (95% CI: 0.823 to 0.934), and the calibration curve fitted well. Decision curve analysis (DCA) showed the clinical utility of the nomogram based on GLIM. CONCLUSION: The addition of CRP improved the sensitivity and accuracy of the survival prediction model. The 5-year survival probability of GC patients undergoing radical resection can be reliably predicted by the nomogram presented in this study.

Comprehensive analysis of the effect of structural parameters on erosion wear, structural stress, and deformation of high-pressure double-elbow in shale-gas fracturing.

In field hydraulic fracturing operation of shale gas development, the high pressure and large displacement liquid-particle two-phase fracturing fluid can be forced to change direction many times through high-pressure double-elbow, and be transported from the outlet pipeline of the fracturing pump to the main pipeline. The high-pressure double-elbow is prone to be affected by erosion wear and Fluid-Structure Interaction (FSI), resulting in perforation and fracture, posing a potential safety threat to field operation. In this study, we conducted the erosion wear experiments on 35CrMo steel used for high-pressure double-elbow in shale-gas fracturing. The erosion rates under different impact angles and flow velocities were obtained, and proposed a novel model of erosion prediction for high-pressure double-elbow. Then the numerical investigation was employed to conduct a comprehensive analysis of erosion wear, structural stress and deformation by the coupling of Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). The effects of structural parameters such as connection straight pipe length, pipe inner diameter and fluid turning direction were discussed. The results indicate that with the increase of connection straight pipe length, the flow erosion decreases first then varies little, and the deformation gradually increases. Slight erosion wear but large structural stress and deformation in major inner diameter pipe. And the minimum degree of erosion and flow-induced deformation present with the fluid turning direction of double-elbow as 0°. The study can provide references for the design, installation and detection of high-pressure double-elbow and ensure safety in the process of shale gas fracturing.

Risk prediction of postoperative permanent stroke in acute type A aortic dissection patients with severe common carotid artery stenosis using brain CT perfusion.

Rationale and objectives: To explore the feasibility and predictive utility for neurological outcomes of brain computed tomography perfusion (CTP) for surgically treated acute type A aortic dissection patients with severe common carotid artery stenosis. Materials and methods: Consecutive acute type A aortic dissection patients with severe common carotid artery stenosis undergoing preoperative brain computed tomography perfusion and surgery at our center were examined in retrospect. Brain perfusion was assessed using parameters including cerebral blood flow, cerebral blood volume, mean transmit time, time to maximum, penumbra volume and infarct core volume. Univariable and multivariable regression analyses were performed to identify clinical and imaging predictors associated with postoperative permanent stroke. Results: Out of 44 patients included, 19 patients (43.2 %) presented with postoperative permanent stroke. Univariable analysis revealed that internal carotid artery dissection, cerebral blood flow of the affected side, cerebral blood volume of the affected side, and penumbra volume were implicated in postoperative permanent stroke. Multivariable analysis further showed that cerebral blood flow of the affected side was an independent indicator of a permanent stroke following surgery (odds ratio: 0.820, 95 % confidence interval: 0.684-0.982; p = 0.012). The area under the receiver operating characteristic curve was 0.867 (95 % confidence interval: 0.764-0.970), and the optimal cut-off value was 45.6mL/100 mL/min. Conclusion: Cerebral blood flow of the affected side was an independent indicator of permanent stroke following surgery in acute type A aortic dissection patients with severe common carotid artery stenosis. Brain CTP could be a helpful modality for quantitative evaluation of cerebral malperfusion and neurological prognostication.

Molecular precision medicine: Multi-omics-based stratification model for acute myeloid leukemia.

Acute myeloid leukemia (AML), as the most common malignancy of the hematopoietic system, poses challenges in treatment efficacy, relapse, and drug resistance. In this study, we have utilized 151 RNA sequencing datasets, 194 DNA methylation datasets, and 200 somatic mutation datasets from the AML cohort in the TCGA database to develop a multi-omics stratification model. This model enables comparison of prognosis, clinical features, gene mutations, immune microenvironment and drug sensitivity across subgroups. External validation datasets have been sourced from the GEO database, which includes 562 mRNA datasets and 136 miRNA datasets from 984 adult AML patients. Through multi-omics-based stratification model, we classified 126 AML patients into 4 clusters (CS). CS4 had the best prognosis, with the youngest age, highest M3 subtype proportion, fewest copy number alterations, and common mutations in WT1, FLT3, and KIT genes. It showed sensitivity to HDAC inhibitors and BCL-2 inhibitors. Both the M3 subtype and CS4 were identified as independent protective factors for survival. Conversely, CS3 had the worst prognosis due to older age, high copy number alterations, and frequent mutations in RUNX1, DNMT3A, and TP53 genes. Additionally, it showed higher proportions of cytotoxic cells and Tregs, suggesting potential sensitivity to mTOR inhibitors. CS1 had a better prognosis than CS2, with more copy number alterations, while CS2 had higher monocyte proportions. CS1 showed good sensitivity to cytarabine, while CS2 was sensitive to RXR agonists. Both CS1 and CS2, which predominantly featured mutations in FLT3, NPM1, and DNMT3A genes, benefited from FLT3 inhibitors. Using the Kappa test, our stratification model underwent robust validation in the miRNA and mRNA external validation datasets. With advancements in sequencing technology and machine learning algorithms, AML is poised to transition towards multi-omics precision medicine in the future. We aspire for our study to offer new perspectives on multi-drug combination clinical trials and multi-targeted precision medicine for AML.

Bilateral Foot Drop Caused by Acute-onset Neuropathy after Diabetic Ketoacidosis: Successful Management and Long-term Follow-up for Employment.

Background: Peripheral neuropathy is a common complication of diabetes, impacting many patients with type 1 or 2 diabetes. Acute-onset peripheral neuropathy after diabetic ketoacidosis (DKA) is rare yet serious, and reports on long-term functional outcomes and rehabilitation for this condition are limited. We present a case of bilateral foot drop caused by acute-onset peripheral neuropathy following DKA. The case was effectively managed through prompt and continuous intervention. Case: A 21-year-old male university student with no notable medical history who was seeking employment presented with impaired consciousness. DKA associated with type 1 diabetes was diagnosed. As blood glucose and acidosis improved, he rapidly regained consciousness. On Day 3 post-onset, bilateral foot drop and lower leg sensory impairment emerged, with nerve conduction studies indicating lower extremity peripheral neuropathy on Day 8. Improvement during hospitalization was modest, so ankle-foot orthoses were prescribed on Day 10. He could walk independently with the orthoses on Day 12 and was discharged home on Day 15. Outpatient follow-up was continued to support the patient's efforts to gain employment. Needle electromyography in the tibialis anterior muscles bilaterally showed denervation at 2 months and polyphasic potentials at 8 months. In the 2 years post-onset, bilateral lower limb muscle strength progressively improved, and the patient successfully secured clerical employment. Discussion: Successful rehabilitation for employment was achieved in the rare condition of acute-onset neuropathy after DKA through effective management based on early orthotic prescription, clinical and electrophysiological examinations, and continuous follow-up.

Predicting clinically significant prostate cancer in elderly patients: A nomogram approach with shear wave elastography.

PURPOSE: This study was to construct a nomogram utilizing shear wave elastography and assess its efficacy in detecting clinically significant prostate cancer (csPCa). METHODS: 290 elderly people with suspected PCa who received prostate biopsy and shear wave elastography (SWE) imaging were respectively registered from April 2022 to December 2023. The elderly participants were stratified into two groups: those with csPCa and those without csPCa, which encompassed cases of clinically insignificant prostate cancer (cisPCa) and non-prostate cancer tissue, as determined by pathology findings. The LASSO algorithm, known as the least absolute shrinkage and selection operator, was utilized to identify features. Logistic regression analysis was utilized to establish models. Receiver operating characteristic (ROC) and calibration curves were utilized to evaluate the discriminatory ability of the nomogram. Bootstrap (1000 bootstrap iterations) was employed for internal validation and comparison with two models. A decision curve and a clinical impact curve were employed to assess the clinical usefulness. RESULTS: Our nomogram, which contained Emean, ΔEmean, prostate volume, prostate-specific antigen density (PSAD), and transrectal ultrasound (TRUS), showed better discrimination (AUC = 0.89; 95% CI: 0.83-0.94), compared to the clinical model without SWE parameters (p = 0.0007). Its accuracy, sensitivity and specificity were 0.83, 0.89 and 0.78, respectively. Based on the analysis of decision curve, the thresholds ranged from 5% to 90%. According to our nomogram, biopsying patients at a 20% probability threshold resulted in a 25% reduction in biopsies without missing any csPCa. The clinical impact curve demonstrated that the nomogram's predicted outcome is closer to the observed outcome when the probability threshold reaches 20% or greater. CONCLUSION: Our nomogram demonstrates efficacy in identifying elderly individuals with clinically significant prostate cancer, thereby facilitating informed clinical decision-making based on diagnostic outcomes and potential clinical benefits.

A comprehensive prognostic and immunological implications of PFKP in pan-cancer.

BACKGROUND: Phosphofructokinase P (PFKP) is a key rate-limiting enzyme in glycolysis, playing a crucial role in various pathophysiological processes. However, its specific function in tumors remains unclear. This study aims to evaluate the expression and specific role of PFKP across multiple tumor types (Pan-cancer) and to explore its potential clinical significance as a therapeutic target in cancer treatment. METHODS: We analyzed the expression of PFKP, immune cell infiltration, and patient prognosis across various cancers using data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Additionally, we conducted a series of experiments in lung cancer cells, including Western blot, CCK-8 assay, colony formation assay, transwell migration assay, scratch wound healing assay, LDH release assay, and flow cytometry, to evaluate the impact of PFKP on tumor cells. RESULTS: PFKP was found to be highly expressed in most cancers and identified as a prognostic risk factor. Elevated PFKP expression is associated with poorer clinical outcomes, particularly in lung adenocarcinoma (LUAD). Receiver operating characteristic (ROC) curve analysis indicated that PFKP can effectively differentiate between cancerous and normal tissues. The expression of PFKP in most tumors showed significant correlations with tumor mutational burden (TMB), microsatellite instability (MSI), immune score, and immune cell infiltration. In vitro experiments demonstrated that PFKP overexpression promotes lung cancer cell proliferation and migration while inhibiting apoptosis, whereas PFKP deficiency results in the opposite effects. CONCLUSION: PFKP acts as an oncogene involved in tumorigenesis and may influence the immune microenvironment within the tumor. Our findings suggest that PFKP could serve as a potential biomarker for predicting prognosis and the efficacy of immunotherapy in tumors.

CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.

Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

Prediction models for risk assessment of surgical site infection after spinal surgery: A systematic review.

Background: Spinal surgeries are a common procedure, but there is significant risk of adverse events following these operations. While the rate of adverse events ranges from 8% to 18%, surgical site infections (SSIs) alone occur in between 1% and 4% of spinal surgeries. Methods: We completed a systematic review addressing factors that contribute to surgical site infection after spinal surgery. From the included studies, we separated the articles into groups based on whether they propose a clinical predictive tool or model. We then compared the prediction variables, model development, model validation, and model performance. Results: About 47 articles were included in this study: 10 proposed a model and 5 validated a model. The models were developed from 7,720 participants in total and 210 participants with SSI. Only one of the proposed models was externally validated by an independent group. The other 4 validation papers examined the performance of the ACS NSQIP surgical risk calculator. Conclusions: While some preoperative risk models have been validated, and even successfully implemented clinically, the significance of postoperative SSIs and the unique susceptibility of spine surgery patients merits the development of a spine-specific preoperative risk model. Additionally, comprehensive and stratified risk modeling for SSI would be of invaluable clinical utility and greatly improve the field of spine surgery.

Composite Prediction Score to Interpret Bone Focal Uptake in Hormone-Sensitive Prostate Cancer Patients Imaged with [18F]PSMA-1007 PET/CT.

Unspecific bone uptake (UBU) related to [18F]PSMA-1007 PET/CT imaging represents a clinical challenge. We aimed to assess whether a combination of clinical, biochemical, and imaging parameters could predict skeletal metastases in patients with [18F]PSMA-1007 bone focal uptake, aiding in result interpretation. Methods: We retrospectively analyzed [18F]PSMA-1007 PET/CT performed in hormone-sensitive prostate cancer (PCa) patients at 3 tertiary-level cancer centers. A fourth center was involved in performing an external validation. For each, a volume of interest was drawn using a threshold method to extract SUVmax, SUVmean, PSMA tumor volume, and total lesion PSMA. The same volume of interest was applied to CT images to calculate the mean Hounsfield units (HUmean) and maximum Hounsfield units. Clinical and laboratory data were collected from electronic medical records. A composite reference standard, including follow-up histopathology, biochemistry, and imaging data, was used to distinguish between PCa bone metastases and UBU. PET readers with less (n = 2) or more (n = 2) experience, masked to the reference standard, were asked to visually rate a subset of focal bone uptake (n = 178) as PCa metastases or not. Results: In total, 448 bone [18F]PSMA-1007 focal uptake specimens were identified in 267 PCa patients. Of the 448 uptake samples, 188 (41.9%) corresponded to PCa metastases. Ongoing androgen deprivation therapy at PET/CT (P < 0.001) with determination of SUVmax (P < 0.001) and HUmean (P < 0.001) independently predicted bone metastases. A composite prediction score, the bone uptake metastatic probability (BUMP) score, achieving an area under the receiver-operating-characteristic curve (AUC) of 0.87, was validated through a 10-fold internal and external validation (n = 89 bone uptake, 51% metastatic; AUC, 0.92). The BUMP score's AUC was significantly higher than that of HUmean (AUC, 0.62) and remained high among lesions with HUmean in the first tertile (AUC, 0.80). A decision-curve analysis showed a higher net benefit with the score. Compared with the visual assessment, the BUMP score provided added value in terms of specificity in less-experienced PET readers (88% vs. 54%, P < 0.001). Conclusion: The BUMP score accurately distinguished UBU from bone metastases in PCa patients with [18F]PSMA-1007 focal bone uptake at PET imaging, offering additional value compared with the simple assessment of the osteoblastic CT correlate. Its use could help clinicians interpret imaging results, particularly those with less experience, potentially reducing the risk of patient overstaging.

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.

Correlation between coronary calcification and cardiac structure in non-dialysis patients with chronic kidney disease.

AIMS: We aim to explore the correlation between coronary artery calcification (CAC) score (CACS) and cardiac structure and function in chronic kidney disease (CKD) patients, create a clinical prediction model for severe CAC associated with cardiac ultrasound indexes. METHODS AND RESULTS: The study included 178 non-dialysis CKD patients who underwent CACS testing and collected general information, serological indices, cardiac ultrasound findings and follow-up on renal function, heart failure (HF) manifestations and re-hospitalization. The mean age of participants in the study cohort was 67.4 years; 59% were male, and 66.9% of patients had varying degrees of comorbid CAC. CKD patients with CACS > 100 were older, predominantly male and had a higher proportion of smoking, diabetes and hypertension (P < 0.05) compared with those with CACS = 0 and 0 < CACS ≤ 100, and had higher brain natriuretic peptide, serum magnesium and fibrinogen levels were also higher (P < 0.05). CACS was positively correlated with left atrial inner diameter (LAD), left ventricular end-diastolic inner diameter (LVDd), left ventricular volume at diastole (LVVd), output per beat (SV) and mitral orifice early diastolic blood flow velocity/early mitral annular diastolic myocardial motion velocity (E/e) (P < 0.05). We tested the associations between varying degrees of CAC and HF and heart valve calcification using multivariable-adjusted regression models. The risk of HF in patients with severe CAC was about 1.95 times higher than that in patients without coronary calcification, and the risk of heart valve calcification was 2.46 times higher than that in patients without coronary calcification. Heart valve calcification and HF diagnosis, LAD and LVDd are essential in predicting severe CAC. During a mean follow-up time of 18.26 ± 10.17 months, 65 (36.52%) patients had a composite renal endpoint event, of which 36 (20.22%) were admitted to renal replacement therapy. Patients with severe CAC had a higher risk of progression of renal function, re-admission due to cardiovascular and renal events and more pronounced symptoms of HF (P < 0.05). CONCLUSIONS: There is a correlation between CACS and cardiac structure and function in non-dialysis CKD patients, which may mainly involve abnormalities in left ventricular structure and cardiac diastolic function. CAC may affect renal prognosis and quality of survival in CKD patients. Based on clinical information, HF, valvular calcification status and indicators related to left ventricular hypertrophy can identify people at risk for severe CAC.