BACKGROUND: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.
METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC).
RESULTS: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUC: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians.
CONCLUSIONS: This study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

BACKGROUND: Many effective therapies for psoriasis are being applied in clinical practice in recent years, however, some patients still can\'t achieve satisfied effect even with biologics. Therefore, it is crucial to identify factors associated with the treatment efficacy among psoriasis patients. This study aims to explore factors influencing the treatment efficacy of psoriasis patients based on decision tree model and logistic regression.
METHODS: We implemented an observational study and recruited 512 psoriasis patients in Shanghai Skin Diseases Hospital from 2021 to 2022. We used face-to-face questionnaire interview and physical examination to collect data. Influencing factors of treatment efficacy were analyzed by using logistic regression, and decision tree model based on the CART algorithm. The receiver operator curve (ROC) was plotted for model evaluation and the statistical significance was set at P < 0.05.
RESULTS: The 512 patients were predominately males (72.1%), with a median age of 47.5 years. In this study, 245 patients achieved ≥ 75% improvement in psoriasis area and severity index (PASI) score in week 8 and was identified as treatment success (47.9%). Logistic regression analysis showed that patients with senior high school and above, without psoriasis family history, without tobacco smoking and alcohol drinking had higher percentage of treatment success in patients with psoriasis. The final decision tree model contained four layers with a total of seventeen nodes. Nine classification rules were extracted and five factors associated with treatment efficacy were screened, which indicated tobacco smoking was the most critical variable for treatment efficacy prediction. Model evaluation by ROC showed that the area under curve (AUC) was 0.79 (95%CI: 0.75 ~ 0.83) both for logistic regression model (0.80 sensitivity and 0.69 specificity) and decision tree model (0.77 sensitivity and 0.73 specificity).
CONCLUSIONS: Psoriasis patients with higher education, without tobacco smoking, alcohol drinking and psoriasis family history had better treatment efficacy. Decision tree model had similar predicting effect with the logistic regression model, but with higher feasibility due to the nature of simple, intuitive, and easy to understand.

UNASSIGNED: To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.
UNASSIGNED: Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.
UNASSIGNED: Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944-0.978) and 0.902 (95% CI:0.840-0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.
UNASSIGNED: The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.

Signal multiplexing is necessary to reduce a large number of readout channels in positron emission tomography (PET) scanners to minimize cost and achieve lower power consumption. However, the conventional weighted average energy method cannot localize the multiplexed events and more sophisticated approaches are necessary for accurate demultiplexing. The purpose of this paper is to propose a non-parametric decision tree model for demultiplexing signals in prismatoid PET (Prism-PET) detector module that consisted of 16 × 16 lutetium yttrium oxyorthosilicate (LYSO) scintillation crystal array coupled to 8 × 8 silicon photomultiplier (SiPM) pixels with 64:16 multiplexed readout. A total of 64 regression trees were trained individually to demultiplex the encoded readouts for each SiPM pixel. The Center of Gravity (CoG) and Truncated Center of Gravity (TCoG) methods were utilized for crystal identification based on the demultiplexed pixels. The flood histogram, energy resolution, and depth-of-interaction (DOI) resolution were measured for comparison using with and without multiplexed readouts. In conclusion, our proposed decision tree model achieved accurate results for signal demultiplexing, and thus maintained the Prism-PET detector module\'s high spatial and DOI resolution performance while using our unique light-sharing-based multiplexed readout.

The challenge of methotrexate (MTX) resistance among low-risk gestational trophoblastic neoplasia (GTN) patients has always been prominent. Despite the International Federation of Gynaecology and Obstetrics (FIGO) score of 0-4 patients comprising the majority of low-risk GTN patients, a comprehensive exploration of the prevalence and risk factors associated with MTX resistance has been limited. Therefore, we aimed to identify associated risk factors in GTN patients with a FIGO score of 0-4. Between January 2005 and December 2020, 310 low-risk GTN patients received primary MTX chemotherapy in two hospitals, with 265 having a FIGO score of 0-4. In the FIGO 0-4 subgroup, 94 (35.5%) were resistant to MTX chemotherapy, and 34 (12.8%) needed multi-agent chemotherapy. Clinicopathologic diagnosis of postmolar choriocarcinoma (OR = 17.18, 95% CI: 4.64-63.70, P < 0.001) and higher pretreatment human chorionic gonadotropin concentration on a logarithmic scale (log-hCG concentration) (OR = 18.11, 95% CI: 3.72-88.15, P < 0.001) were identified as independent risk factors associated with MTX resistance according to multivariable logistic regression. The decision tree model and regression model were developed to predict the risk of MTX resistance in GTN patients with a FIGO score of 0-4. Evaluation of model discrimination, calibration and net benefit revealed the superiority of the decision tree model, which comprised clinicopathologic diagnosis and pretreatment hCG concentration. The patients in the high- and medium-risk groups of the decision tree model had a higher probability of MTX resistance. This study represents the investigation into MTX resistance in GTN patients with a FIGO score of 0-4 and disclosed a remission rate of approximately 65% with MTX chemotherapy. Higher pretreatment hCG concentration and clinicopathologic diagnosis of postmolar choriocarcinoma were independent risk factors associated with resistance to MTX chemotherapy. The decision tree model demonstrated enhanced predictive capabilities regarding the risk of MTX resistance and can serve as a valuable tool to guide the clinical treatment decisions for GTN patients with a FIGO score of 0-4.

BACKGROUND: Infants born at extremely preterm gestational ages are typically admitted to the neonatal intensive care unit (NICU) after initial resuscitation. The subsequent hospital course can be highly variable, and despite counseling aided by available risk calculators, there are significant challenges with shared decision-making regarding life support and transition to end-of-life care. Improving predictive models can help providers and families navigate these unique challenges.
OBJECTIVE: Machine learning methods have previously demonstrated added predictive value for determining intensive care unit outcomes, and their use allows consideration of a greater number of factors that potentially influence newborn outcomes, such as maternal characteristics. Machine learning-based models were analyzed for their ability to predict the survival of extremely preterm neonates at initial admission.
METHODS: Maternal and newborn information was extracted from the health records of infants born between 23 and 29 weeks of gestation in the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database. Applicable machine learning models predicting survival during the initial NICU admission were developed and compared. The same type of model was also examined using only features that would be available prepartum for the purpose of survival prediction prior to an anticipated preterm birth. Features most correlated with the predicted outcome were determined when possible for each model.
RESULTS: Of included patients, 37 of 459 (8.1%) expired. The resulting random forest model showed higher predictive performance than the frequently used Score for Neonatal Acute Physiology With Perinatal Extension II (SNAPPE-II) NICU model when considering extremely preterm infants of very low birth weight. Several other machine learning models were found to have good performance but did not show a statistically significant difference from previously available models in this study. Feature importance varied by model, and those of greater importance included gestational age; birth weight; initial oxygenation level; elements of the APGAR (appearance, pulse, grimace, activity, and respiration) score; and amount of blood pressure support. Important prepartum features also included maternal age, steroid administration, and the presence of pregnancy complications.
CONCLUSIONS: Machine learning methods have the potential to provide robust prediction of survival in the context of extremely preterm births and allow for consideration of additional factors such as maternal clinical and socioeconomic information. Evaluation of larger, more diverse data sets may provide additional clarity on comparative performance.

The domesticated silkworm, Bombyx mori Linnaeus (Lepidoptera: Bombycidae), often poses a challenge in strain identification due to similarities in morphology and genetic background. In South Korea, around 40 silkworm strains are classified as premium, including 5 endemic tri-molting strains: Goryeosammyeon, Sammyeonhonghoeback, Hansammyeon, Sun7ho, and Sandongsammyeon. These strains have potential for breeding programs in response to emerging industry demands, necessitating a reliable strain identification method. In this study, we established a molecular diagnosis approach for these 5 strains. We selected 2-4 single-nucleotide polymorphisms (SNPs) for each strain from whole-genome sequences of 39 strains, encompassing 37 previously studied and 2 newly added. These SNPs were utilized to construct decision trees for each endemic strain identification. The SNPs can be used to distinguish each target strain from the 38 nontarget strains by the tetra-primer amplification refractory mutation system-polymerase chain reaction, with the exception of HMS which needs the addition of PCR-restriction fragment length polymorphism method at the final step. This decision tree-based method using genomic SNPs, coupled with the 2 typing methods, produced consistent and accurate results, providing 100% accuracy. Additionally, the significant number of remaining SNPs identified in this study could be valuable for future diagnosis of the other strains.

Maternal syphilis not only seriously affects the quality of life of pregnant women themselves but also may cause various adverse pregnancy outcomes (APOs). This study aimed to analyse the association between the related factors and APOs in maternal syphilis. 7,030 pregnant women infected with syphilis in Henan Province between January 2016 and December 2022 were selected as participants. Information on their demographic and clinical characteristics, treatment status, and pregnancy outcomes was collected. Multivariate logistic regression models and chi-squared automatic interaction detector (CHAID) decision tree models were used to analyse the factors associated with APOs. The multivariate logistic regression results showed that the syphilis infection history (OR = 1.207, 95% CI, 1.035-1.409), the occurrence of abnormality during pregnancy (OR = 5.001, 95% CI, 4.203-5.951), not receiving standard treatment (OR = 1.370, 95% CI, 1.095-1.716), not receiving any treatment (OR = 1.313, 95% CI, 1.105-1.559), and a titre ≥1:8 at diagnosis (OR = 1.350, 95%CI, 1.079-1.690) and before delivery (OR = 1.985, 95%CI, 1.463-2.694) were risk factors. A total of six influencing factors of APOs in syphilis-infected women were screened using the CHAID decision tree model. Integrated prevention measures such as early screening, scientific eugenics assessment, and standard syphilis treatment are of great significance in reducing the incidence of APOs for pregnant women infected with syphilis.

To develop a decision tree model based on clinical information, molecular genetics information and pre-operative magnetic resonance imaging (MRI) radiomics-score (Rad-score) to investigate its predictive value for the risk of recurrence of glioblastoma (GBM) within one year after total resection. Patients with pathologically confirmed GBM at Huashan Hospital, Fudan University between November 2017 and June 2020 were retrospectively analyzed, and the enrolled patients were randomly divided into training and test sets according to the ratio of 3:1. The relevant clinical and MRI data of patients before, after surgery and follow-up were collected, and after feature extraction on preoperative MRI, the LASSO filter was used to filter the features and establish the Rad-score. Using the training set, a decision tree model for predicting recurrence of GBM within one year after total resection was established by the C5.0 algorithm, and scatter plots were generated to evaluate the prediction accuracy of the decision tree during model testing. The prediction performance of the model was also evaluated by calculating area under the receiver operating characteristic (ROC) curve (AUC), ACC, Sensitivity (SEN), Specificity (SPE) and other indicators. Besides, two external validation datasets from Wuhan union hospital and the second affiliated hospital of Xuzhou Medical University were used to verify the reliability and accuracy of the prediction model. According to the inclusion and exclusion criteria, 134 patients with GBM were finally identified for inclusion in the study, and 53 patients recurred within one year after total resection, with a mean recurrence time of 5.6 months. According to the importance of the predictor variables, a decision tree model for predicting recurrence based on five important factors, including patient age, Rad-score, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, pre-operative Karnofsky Performance Status (KPS) and Telomerase reverse transcriptase (TERT) promoter mutation, was developed. The AUCs of the model in the training and test sets were 0.850 and 0.719, respectively, and the scatter plot showed excellent consistency. In addition, the prediction model achieved AUCs of 0.810 and 0.702 in two external validation datasets from Wuhan union hospital and the second affiliated hospital of Xuzhou Medical University, respectively. The decision tree model based on clinicopathological risk factors and preoperative MRI Rad-score can accurately predict the risk of recurrence of GBM within one year after total resection, which can further guide the clinical optimization of patient treatment decisions, as well as refine the clinical management of patients and improve their prognoses to a certain extent.

The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is essential for determining the scope of surgical resection. It also affects the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE). Non-invasive tools, including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4), and γ-glutamyl transferase to platelet ratio (GPR), are less accurate in predicting cirrhosis in HCC patients. We aimed to build a novel decision tree model to improve diagnostic accuracy of liver cirrhosis.
The Mann-Whitney U test, χ2 test, and multivariate logistic regression analysis were used to identify independent cirrhosis predictors. A decision tree model was developed using machine learning algorithms in a training cohort of 141 HCC patients. Internal validation was conducted in 99 HCC patients. The diagnostic accuracy and calibration of the established model were evaluated using receiver operating characteristic (ROC) and calibration curves, respectively.
Sex and platelet count were identified as independent cirrhosis predictors. A decision tree model integrating imaging-reported cirrhosis, APRI, FIB-4, and GPR was established. The novel model had an excellent diagnostic performance in the training and validation cohorts, with area under the curve (AUC) values of 0.853 and 0.817, respectively. Calibration curves and the Hosmer-Lemeshow test showed good calibration of the novel model. The decision curve analysis (DCA) indicated that the decision tree model could provide a larger net benefit to predict liver cirrhosis.
Our developed decision tree model could successfully predict liver cirrhosis in HCC patients, which may be helpful in clinical decision-making.