Significant Clinical Risk Factors Research Articles

PREDICTIVE VALUE ANALYSIS OF MR IMAGING FEATURES ON THE RISK OF KNEE REPLACEMENT IN PATIENTS WITH KNEE ARTHRITIS

This study used knee MR imaging features to quantify the severity of knee injury in patients and analyzed the predictive value of knee MR imaging features in their risk of knee replacement. A total of 120 patients with knee arthritis were included from a public knee arthritis database FNIH OAI. First, univariate logistic regression was used to screen clinical features to obtain the clinical risk factors. Then the minimum redundancy maximum correlation (mRMR) method was used to reduce the image features, and the LASSO method was used to further screen the retained image features to construct a Rad model. Next, the multivariate logistic regression method was used to combine the Rad model and the screened clinical risk factors to construct a combined model and its corresponding nomogram. Finally, ROC curve and its related metrics, Hosmer–Lemeshow test and Delong test were used to evaluate and compare the accuracy and consistency of the model performance. Age and body mass index (BMI) were found as significant clinical risk factors for knee replacement. After using mRMR and LASSO methods, 147 image features were reduced to 30 and 7 features, respectively, then these 7 features were linearly combined to construct the Rad model. Age, BMI, and the Rad model were combined to establish the combined model and its corresponding nomogram. The resulted two models showed high accuracy ([Formula: see text]) and consistency (H-L test: [Formula: see text]) on both the training set and test set. Finally, the comparison results showed that the prediction performance of the combined model was better than that of the Rad model, but their difference was not significant (Delong test: [Formula: see text]). This paper studied the predictive performance of MR imaging features on the risk of knee replacement and built a model that can be used to predict the potential risk of knee replacement in patients with knee arthritis. The resulting models showed good predictive accuracy and consistency. The drawn nomogram could be used as a useful tool to personalize the prognosis of patients with knee arthritis and guide clinical decisions on knee replacement.

139. DOES POSTOPERATIVE NON-INVASIVE VENTILATION INCREASE THE RISK FOR COMPLICATIONS IN CHILDREN POST-REPAIR OF ESOPHAGEAL ATRESIA?

Abstract The primary aim of this study was to determine if postoperative use of high flow nasal cannula (HFNC) and/or continuous positive airway pressure (CPAP) are independent risk factors for adverse postoperative outcomes after surgical repair of Esophageal Atresia (EA). Secondary aims were to determine other significant clinical risk factors associated with these adverse outcomes. A retrospective chart review was conducted on all EA neonates repaired and managed postoperatively between 2007-2020 across two centres: Sydney Children’s Hospital (SCH) and The Royal Hospital for Women (RHW). The primary outcome measure was anastomotic leakage (AL), and other outcome measures included anastomotic stricture (AS), mediastinitis, sepsis, and pneumothorax. Significant associations were identified using Chi-square, and univariate and multivariate logistical regression models (p < 0.05). We reviewed 104 charts. Post-repair, all were placed on invasive mechanical ventilation (IMV) (median of 119.50 hours). Of these, 45 (43.3%) were subsequently bridged with some form of NIV post-extubation, with 33 each on HFNC and CPAP. 59 (56.7%) developed at least one postoperative complication: 12 had AL, and 17 had AS. After adjustment, no significant association was found between HFNC or CPAP use with development of AL (p = 0.074 and p = 0.859), or any other adverse outcome. Independent risk factors for AL include anastomosis under tension and type B EA. Delayed first oral intake significantly increased risk of mediastinitis and sepsis. Our results show no increased risk of AL or any other adverse postoperative event with use of non-invasive ventilation in form of HFNC and/or CPAP. Reducing anastomotic tension during primary repair of OA-TOF and introducing early oral feeding appears to be protective and reduces risk of AL and infection. However, further prospective research is still needed to help guide post extubation ventilation strategies in this population.

Clinical and Radiological Characteristics for Recurrence of Chronic Subdural Hematoma: A Systematic Review and Meta-Analysis.

Chronic subdural hematoma (cSDH) is one of the most studied clinical entities in the neurosurgical literature. Management of cSDH is complicated by its propensity to recurrence. Various factors for the development of recurrence of cSDH have been described in various clinical, epidemiological, and observational studies, yet the evidence available is limited. A systematic review and meta-analysis as per PRISMA guidelines to identify clinical and radiological factors which can predict the development of recurrence in cSDH. A total of 14 studies were included for the systematic review and meta-analysis after a comprehensive search of the online databases. Eight studies were of high methodological quality. Age, use of anticoagulants, obesity, seizure, and liver disease were found to be statistically significant clinical risk factors for the development of recurrence in cSDH. Among the radiological parameters, the internal structure of the hematoma and the width of the hematoma was found to be significant risk factor predicting the development of recurrence. Age >75 years, use of anticoagulation therapy, liver disease, and obesity were significant risk factors for cSDH recurrence. Pneumocephalus, internal architecture of hematoma, bilateral cSDH, the width of hematoma, and the presence of bilateral cSDH are important radiological parameters of the development of recurrent cSDH

Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma

BackgroundThe determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status.MethodsWe retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 (http://keyan.deepwise.com/), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA).Results70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827–0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758–0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904–0.987) in the training set and 0.868 (95%CI: 0.789–0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer–Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram.ConclusionPost largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction.

One-hour post-load glucose is associated with severity of hepatic fibrosis risk

AimIndividuals with high 1-hour post-load glucose (1-h PG > 155 mg/dl; 8.6 mmol/l) during an oral glucose tolerance test are at increased risk of type 2 diabetes (T2D) and cardiovascular complications, hepatic steatosis, and mortality. However,the clinical relevance of 1-h PG for the severity of hepatic fibrosis risk remains undefined. MethodsCross-sectional data of the CATAMERI study (n = 2335) were analyzed. Participants underwent anthropometric measurements, liver enzyme determinations, cardiometabolic profiling, and a75-gram oral glucose tolerance test, including fasting, 1-h and 2-h PG determinations and measurement of FIB-4 score to assess degree of hepatic fibrosis. Multivariable logistic regression analysis was performed to evaluate risk of advanced hepatic fibrosis with worsening glycemic status. ResultsWe stratifiedthe study group into 6 categories based on glycemic status: normal glucose tolerance (NGT) 1h-PG Low, NGT 1h-PG High, iIFG 1h-PG Low, iIFG 1h-PG High, IGT, and newly detected T2D. Anthropometric and cardiometabolic profiles worsened gradually with glycemic status. Moreover, compared to NGT-1h-PG Low group, worsening glycemic status was significantly associated with the severity of fibrosis, independent of other significant clinical risk factors. Conclusions1-PG is a valuable tool for stratifying subjects with NGT or IFG at heightened risk of hepatic fibrosis requiring further evaluation with elastography.

Routine Use of Cerebral Magnetic Resonance Imaging in Infants Born Extremely Preterm

To describe cerebral abnormalities and their risk factors in a contemporary cohort of infants born extremely premature after the introduction of routine cerebral magnetic resonance imaging (cMRI) at term-equivalent age. All cMRI examinations performed during November 2017 and November 2020, based on a standardized neonatal cMRI protocol, were included into analysis. Pathologies were retrospectively classified into 3 categories: intraventricular hemorrhage (IVH), white matter disease, and cerebellar injuries. A total of 198 cMRI examinations were available for analyses; 93 (47%) showed abnormalities, most frequently IVH (n=65, 33%), followed by cerebellar injuries (n=41, 21%), and white matter disease (n=28, 14%). Severe abnormalities were found in 18% of patients (n=36). Significant clinical risk factors for abnormalities on cMRI were lower Apgar scores, lower umbilical artery and first neonatal pH, asphyxia, blood culture-proven sepsis (especially late-onset), and prolonged need of respiratory support and supplemental oxygen. After routine cMRI, without preconfirmed pathology by cranial ultrasonography, low-grade IVH, noncystic white matter disease, and cerebellar injuries were the most frequently found abnormalities. The clinical value and long-term benefit of the detection of these low-grade pathologies have yet to be confirmed.

Abstract 5200: The incidence and risk factors of hyperprogressive disease(HPD) in non-small cell lung cancer(NSCLC) treated with immune checkpoint inhibitors(ICI)

Abstract Background: HPD is an unexpected rapid acceleration of tumor growth reported for patients treated with ICI. The definition of HPD is not fully established and risk factors are uncertain. Our aim in this study was to find the incidence of HPD as well as its significant clinical and genetic risk factors. Method: A total of 203 patients with NSCLC who received ICI at Northwestern Memorial Hospital during 2008-2020 were included. Tumor growth kinetics (TGK) on immunotherapy and TGK pre-immunotherapy were collected and TGK ratio (TGKR) was calculated. HPD was defined as TGKR≥2. Result: HPD was observed in 16% (n=33) of patients. HPD was significantly associated with high ECOG (3-4 vs 1-2) [OR = 4.52, 95% CI = 1.31 to 16.29, P = 0.01], presence of bone metastasis [OR = 2.51, 95% CI = 1.10 to 5.28, P = 0.01], neutrophils/lymphocyte ratio(NLR) ≥5 [OR = 2.68, 95% CI = 1.24 to 5.81 P = 0.01], thrombocytosis (platelet count >450k) [OR = 5.31, 95% CI = 2.07 to 14.70, P=0.0003], and treatment with PD-L1 inhibitor vs PD-1 inhibitor [OR = 2.14, 95% CI= 0.97 to 4.68, P= 0.068]. HPD was inversely associated with positive PD-L1 expression [OR = 0.34, 95% CI = 0.13 to 0.86, P = 0.01], ICI combined with chemotherapy vs ICI monotherapy [OR = 0.24, 95% CI = 0.055 to 0.97, P=0.04], and positive TTF-1 expression [OR = 0.46, 95% CI = 0.21 to 1.02, P=0.057]. HPD was not associated with histology of NSCLC (adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and large cell neuroendocrine carcinoma), age(<70, ≥70), sex, smoking history, number of metastatic lesions (<3, ≥3), brain metastasis, liver metastasis, brain metastasis before treatment initiation, EGFR mutation, TP53 mutation, BRAF mutation, and TNM staging. Conclusion: The incidence of HPD in NSCLC treated with ICI was consistent with current literature. High NLR and platelet count are suggestive of an immunosuppressive environment that could contribute to the rapid growth of tumors. To our knowledge, this is the first study to report an inverse correlation of PD-L1 expression and TTF-1 expression with HPD. The strength of our study lies in a large number of patients. Further studies on validating risk factors of HPD and exploring their associated mechanisms are warranted. Citation Format: Hyeonseon Kim, Yeun Ho Lee, Chiwoo Song, Leeseul Kim, Min Jeong Kim, Horyun Choi, Jinah Kim, Young Kwang Chae. The incidence and risk factors of hyperprogressive disease(HPD) in non-small cell lung cancer(NSCLC) treated with immune checkpoint inhibitors(ICI) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5200.

ATRT-20. Novel prognostic molecular signatures for improved risk-classification of Atypical Teratoid Rhabdoid Tumours

Abstract Malignant Rhabdoid Tumours (MRT) are aggressive paediatric malignancies seen in the central nervous system (Atypical Teratoid Rhabdoid Tumours (ATRT)), and kidney and other soft tissues (Extra-cranial Rhabdoid Tumours (ECRT)). With current therapies often proving ineffective and a lack of clear prognostic associations with consensus subgroups, we explored the possibility of using prognostic molecular signatures to further identify the biological characteristics of high risk ATRT patients. By employing a cross-validated feature selection method the methylation profiles of 121 MRT patients were analysed with clinical data to obtain meta-CpG signatures associated with prognosis for ATRT, ECRT and MRT. The relationship between these meta-CpG signatures and the consensus subgroups were further explored, along with the correlation of meta-CpGs with gene expression to establish biological significance. By selecting CpGs for their ability to predict survival this method obtained three novel prognostic methylation signatures which predict MRT outcome (ATRT-5, ECRT-14 and MRT-42). These signatures are independent of molecular subgroup and each signature was significantly associated with overall survival (OS) and event free survival (EFS) in their respective cohorts (p<0.001). Both ATRT-5 and MRT-42 maintained their significant association with OS in an independent ATRT cohort (n=64) and each meta-CPG signature is prognostically independent of other major clinical risk factors (e.g. receipt of radiotherapy and presence of metastases). Biologically, individuals with high-risk methylation signatures showed a gene expression profile suggestive of higher proliferative rates and tumours with low-risk scores in ATRT-5 and MRT-42 had an upregulated inflammatory response and increased immune infiltration. Combining these meta-CpGs with other significant clinical risk-factors produced high performing multivariate Cox-models enabling us to propose new stratification models for ATRT and MRT patients. These subgroup-independent prognostic signatures represent a distinct biology in ATRT and, if validated in prospective studies, could progress the use and efficacy of precision-based medicine in this therapeutically challenging disease.

Real-world analysis of apalutamide-associated skin adverse events in Japanese patients with advanced prostate cancer: a multi-institutional study in the Chu-shikoku Japan Urological Consortium.

Apalutamide-associated skin adverse events are more common in the Japanese than in the global population. However, limited clinical data have hampered further understanding. This real-world study investigated the clinical characteristics of skin adverse events in patients with advanced prostate cancer. We retrospectively reviewed 119 patient records from 16 institutions in Japan. Skin adverse events were graded according to the Common Terminology Criteria for Adverse Events (v5.0). The incidence and characteristics of skin adverse events (along with the clinical risk factors for their incidence, worsening, and recurrence) were evaluated. Fifty-five patients (46.2%) experienced skin adverse events. The median times to the incidence and remission of skin adverse events were 62 and 30days, respectively. Grade 3 skin adverse events were observed in 15 patients (12.6%). The median time from the first incidence to apalutamide interruption was significantly longer in patients with progression to grade 3 skin adverse events than in those without such a progression (8 vs. 0days, p = 0.005). Skin adverse events were observed in 45.2% of patients who resumed apalutamide treatment (median treatment interruption time: 31.5days). Sixteen patients (13.4%) permanently discontinued apalutamide due to skin adverse events. No significant clinical risk factors for the incidence, worsening and recurrence of apalutamide-associated skin adverse events were observed. Nearly half of the Japanese patients in this study experienced skin adverse events following apalutamide administration. The time to apalutamide discontinuation after the incidence of skin adverse events was positively correlated with the worsening of these events.

A Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma ≤ 5 cm.

AimThe aim of this study is to establish and validate a radiomics-based model using preoperative Gd-EOB-DTPA-enhanced MRI to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma ≤ 5 cm.MethodsClinicopathologic and MRI data of 178 patients with solitary hepatocellular carcinoma (HCC) (≤5 cm) were retrospectively collected from a single medical center between May 2017 and November 2020. Patients were randomly assigned into training and test subsets by a ratio of 7:3. Imaging features were extracted from the segmented tumor volume of interest with 1-cm expansion on arterial phase (AP) and hepatobiliary phase (HBP) images. Different models based on the significant clinical risk factors and/or selected imaging features were established and the predictive performance of the models was evaluated.ResultsThree radiomics models, the AP_model, the HBP_model, and the AP+HBP_model, were constructed for MVI prediction. Among them, the AP+HBP_model outperformed the other two. When it was combined with a clinical model, consisting of tumor size and alpha-fetoprotein (AFP), the combined model (AP+HBP+Clin_model) showed an area under the curve of 0.90 and 0.70 in the training and test subsets, respectively. Its sensitivity and specificity were 0.91 and 0.76 in the training subset and 0.60 and 0.79 in the test subset, respectively. The calibration curve illustrated that the combined model possessed a good agreement between the predicted and the actual probabilities.ConclusionsThe radiomics-based model combining imaging features from the arterial and hepatobiliary phases of Gd-EOB-DTPA-enhanced MRI and clinical risk factors provides an effective and reliable tool for the preoperative prediction of MVI in patients with HCC ≤ 5 cm.

Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study.

ObjectiveTo investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).MethodsA total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established.ResultsOf the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively.ConclusionThe DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.

Predictive Modeling of New-Onset Postoperative Diplopia Following Orbital Decompression for Thyroid Eye Disease.

To identify risk factors for the development of new-onset, postoperative diplopia following orbital decompression surgery based on patient demographics, clinical exam characteristics, radiographic parameters, and surgical techniques. We conducted a multi-center retrospective chart review of patients who underwent orbital decompression for thyroid eye disease (TED). Patient demographics, including age, gender, smoking history, preoperative exophthalmometry, clinical activity score (CAS), use of peribulbar and/or systemic steroids, and type of orbital decompression were reviewed. Postoperative diplopia was determined at a minimum of 3 months postoperatively and before any further surgeries. Cross-sectional area ratios of each extraocular muscle to orbit and total fat to orbit were calculated from coronal imaging in a standard fashion. All measurements were carried out using PACS imaging software. Multivariable logistic regression modeling was performed using Stata 14.2 (StataCorp, College Station, TX). A total of 331 patients without preoperative diplopia were identified. At 3 months postoperatively, 249 patients had no diplopia whereas 82 patients developed diplopia. The average postoperative follow-up was 22 months (range 3-156) months. Significant preoperative clinical risk factors for postoperative diplopia included older age at surgery, proptosis, use of peribulbar or systemic steroids, elevated clinical activity score, and presence of preoperative compressive optic neuropathy. Imaging findings of enlarged cross-sectional areas of each rectus muscle to the overall orbital area also conferred a significant risk of postoperative diplopia. Regarding surgical factors, postoperative diplopia was more common among those undergoing medial wall decompression, bilateral orbital surgery, and balanced decompression, whereas endoscopic medial wall decompression was found to be relatively protective. This study identifies risk factors associated with the development of diplopia following orbital decompression using multivariable data. This study demonstrates that several characteristics including age, clinical activity score, the cross-sectional muscle to orbit ratios, in addition to the type of orbital decompression surgery, are predictive factors for the development of new-onset postoperative diplopia.

Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer

BackgroundPreoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images.MethodsA total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities.ResultsTwelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81–0.93) and 0.90 (95% CI, 0.83–0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients.ConclusionThe proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.

Long-Term Evaluation and Normal Tissue Complication Probability (NTCP) Models for Predicting Radiation-Induced Optic Neuropathy after Intensity-Modulated Radiation Therapy (IMRT) for Nasopharyngeal Carcinoma: A Large Retrospective Study in China.

Purpose To quantify the long-term evaluation of optic chiasma (OC) and/or optic nerve(s) (ONs) and to develop predictive models for radiation-induced optic neuropathy (RION) in nasopharyngeal carcinoma after intensity-modulated radiotherapy (IMRT). Methods and Materials A total of 3,662 patients' OC/ONs with full visual acuity and dosimetry data between 2010 and 2015 were identified. Critical dosimetry predictors of RION were chosen by machine learning and penalized regression for survival. A nomogram containing dosimetry and clinical variables was generated for predicting RION-free survival. Results The median follow-up was 71.79 (2.63–120.9) months. Sixty-six eyes in 51 patients (1.39%) developed RION. Two patients were visual field deficient, and 49 patients had visual acuity of less than 0.1 (20/200). The median latency time was 36 (3–90) months. The 3-, 5-, and 8-year cumulative incidence of RION was 0.78%, 1.19%, and 1.97%, respectively. Dmax was the most critical dosimetry variable for RION (AUC: 0.9434, the optimal cutoff: 64.48 Gy). Patients with a Dmax ≥64.48 Gy had a significantly higher risk of RION (HR = 102.25; 95%CI, 24.86–420.59; P < 0.001). Age (>44 years) (HR = 2.234, 95% CI = 1.233–4.051, p = 0.008), advanced T stage (T3 vs. T1-2: HR = 7.516, 95% CI = 1.725–32.767, p=0.007; T4 vs. T1-2: HR = 37.189, 95% CI = 8.796–157.266, P < 0.001), and tumor infiltration/compression of the OC/ONs (HR = 4.572, 95% CI = 1.316–15.874, p=0.017) were significant clinical risk factors of RION. A nomogram comprising age, T stage, tumor infiltration/compression of the OC/ON, and Dmax significantly outperformed the model, with only Dmax predicting RION (C-index: 0.916 vs. 0.880, P < 0.001 in the training set; 0.899 vs. 0.874, P=0.038 in the test set). The nomogram-defined high-risk group had a worse 8-year RION-free survival. Conclusions In the IMRT era, Dmax <60 Gy is safe and represents an acceptable dose constraint for most NPC patients receiving IMRT. A reasonable trade-off for selected patients with unsatisfactory tumor coverage due to proximity to the optic apparatus would be Dmax <65 Gy. Caution should be exercised when treating elderly and advanced T-stage patients or those with tumor infiltration/compression of the OC/ON. Our nomogram shows strong efficacy in predicting RION.

Abstract TP86: Depression And Anxiety After Stroke In A Young Adult Population

Background and Objectives: We aimed to determine the prevalence of poststroke depression (PSD) and anxiety as well as to identify their predictors in a population of young Filipino adults. Methods: We performed a cross-sectional epidemiologic study in the largest tertiary hospital in the Philippines. The study involved the administration of a structured survey tool and review of medical records. The Hospital Anxiety and Depression Score was used to screen for anxiety and depression. Chi-square tests and Fisher’s exact tests were used to compare between groups with anxiety or depression and those without. Multivariable logistic regression analysis was performed to determine significant socio-economic and clinical risk factors of PSD and anxiety. Results: In our study population of 114 young adult stroke patients, the prevalence of depression was 20.2% while the prevalence of anxiety was 34.2%. Significant predictors of PSD were the presence of anxiety (OR 1.84; CI 1.05-3.22), lower mRS scores (mRS 3-5 OR 5.52; 95% CI 1.09-8.03) and diabetes (OR 2.09; 95% CI 1.67-6.26). Meanwhile, significant predictors of poststroke anxiety included depression (OR 7.5; 95% CI 5.02-21.94) and dependence (Barthel Index scores 95-100; OR 0.32; 95% CI 0.14-0.71). Relationship status, educational attainment, stroke subtype and stroke location were not found to be significant predictors of PSD and anxiety. Conclusion: Our study showed that young adult patients who suffer from stroke are at risk for adverse socio-economic and psychiatric consequences. A significant proportion of the population had depression and anxiety after stroke. Clinicians should be aware of these conditions that influence the outcomes of young adult stroke patients.

Evaluation of risk factors for uric acid elevation in COVID-19 patients treated with favipiravir

The objective of this retrospective study was to identify the clinical risk factor associated with uric acid elevation in coronavirus disease (COVID-19) patients treated with favipiravir. Uric acid elevation was defined as an unexplained increase of ≥1.5 times in the patient's uric acid level from baseline. Twenty-nine COVID-19 patients were included in the study. Uric acid elevation developed during favipiravir therapy in 12 (41.4%) patients and the median onset time was 4.5 days after starting favipiravir. In multiple logistic regression analysis, the favipiravir dosage (adjusted OR = 1.69 [1.02–2.81], P = 0.044) and younger patient age (adjusted OR = 0.91 [0.83–0.99], P = 0.040) were significant clinical risk factors for uric acid elevation. No significant between-group difference was noted in the uric acid elevation and non-elevation groups in the clinical recovery after favipiravir therapy. The uric acid levels of patients administered with favipiravir should be monitored closely.

Study of Socioclinical Risk Factors for Breast Milk Deficiency in Women During Lactation

Breastfeeding has benefits for the health and development of the baby, but also for the mother. the aim of this study is to assess certain risk factors of breast milk deficiency in women during lactation. The study was based on 150 voluntary individuals divided into Breastfeeding women with milk with average age of 31.11±4.56 years old and Breastfeeding women without milk with average age of 33.97±6.50 years old. Results of the risk factors show that Obesity, Contraception pills, Psychic stress and food deficit are significant (P &lt;0.05) socioeconomic risk factors that cause loss of milk in breastfeeding women with OR (1.50- 2.85) and that Polycystic ovaries and Hypocalcaemia are significant (P &lt;0.05) clinical risk factors for loss of milk in breastfeeding women with OR (2.08- 2.35). From the above, we conclude that breastfeeding is strongly affected by the lifestyle and some diseases that affect women, which requires sensitivity to improve these elements for safe breastfeeding.

Preoperative Prediction of BRAF Mutation Status in Colorectal Cancer Using a Clinical-radiomics Model

This study aimed to develop a clinically practical model to predict V-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation in colorectal cancer according to radiomic signatures based on computed tomography (CT) and clinical risk factors, and to determine the model's diagnostic accuracy for BRAF mutation status. This retrospective study included 140 patients with colorectal cancer. The significant clinical risk factors were used to build the clinical model; the least absolute shrinkage and selection operator algorithm was adopted to construct a radiomics signature according to imaging features of the tumor lesion, and stepwise logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis. The radscore, generated by 5 selected radiomics features, demonstrated a favorable ability to predict BRAF mutation in both the training (area under the receiver operating characteristic curve [AUC] 0.93) and validation (AUC 0.87) cohorts. Subsequently, integrating two independent predictors (including the radscore and clinical risk factors) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.95 and 0.88 in both cohorts. Moreover, the accuracy for predicting BRAF mutations was higher than that of the clinical model, ranging from 0.70 to 0.89. The proposed CT-based radiomics signature is associated with BRAF mutations. The present study also proposes a combined model can potentially be applied in the individual preoperative prediction of BRAF mutation status in colorectal cancer. CT-based radiomics showed satisfactory diagnostic significance for the BRAF status in colorectal cancer, the clinical-combined model may be applied in the individual preoperative prediction of BRAF mutation.

Analysis of Clinical Characteristics, Radiological Predictors, Pathological Features, and Perioperative Outcomes Associated with Perinephric Fat Adhesion Degree.

Background To assess the clinical characteristics, radiological predictors, and pathological features of perinephric fat adhesion degree (PFAD) graded based on fixed criteria and to determine the impact of adherent perinephric fat (APF) on retroperitoneal laparoscopic partial nephrectomy (RLPN) outcomes. Methods 84 patients undergoing RLPN were included and graded into 4 groups based on PFAD. Univariate and multivariate analyses were performed for clinical characteristics and radiological predictors of PFAD. Perioperative data were compared between APF groups and non-APF groups. Masson staining determined collagen fibers. Immunohistochemistry detected CD45 immune cells and CD34 vessels. Results 20, 28, 18, and 18 patients were graded as normal perinephric fat (NPF), mild adherent perinephric fat (MiPF), moderate adherent perinephric fat (MoPF), and severe adherent perinephric fat (SPF), respectively. Multivariate analysis revealed that gender (p < 0.001), age (p = 0.003), and hypertension (p = 0.006) were significant clinical risk factors of PFAD, while radiological predictors included perinephric stranding (p = 0.001), posterior perinephric fat thickness (p = 0.009), and perinephric fat density (p = 0.02). APF was associated with drain output (p = 0.012) and accompanied by immune cells gathering in renal cortex near thickened renal capsule with many vessels. Conclusions Clinical characteristics and radiological predictors can evaluate PFAD and may assist to guide preoperative surgical option. Pathological features of APF reflect decapsulation and bleeding during kidney mobilization at RLPN.

Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort.

BackgroundNonalcoholic fatty liver disease (NAFLD) is the most prevalent liver disease worldwide. Cardiovascular disease (CVD) is the leading cause of mortality among patients with NAFLD. The aim of our study was to develop a machine learning algorithm integrating clinical, lifestyle, and genetic risk factors to identify CVD in patients with NAFLD.Methods and ResultsWe created a cohort of patients with NAFLD from the UK Biobank, diagnosed according to proton density fat fraction from magnetic resonance imaging data sets. A total of 400 patients with NAFLD with subclinical atherosclerosis or clinical CVD, defined by disease codes, constituted cases and 446 NAFLD cases with no CVD constituted controls. We evaluated 7 different supervised machine learning approaches on clinical, lifestyle, and genetic variables for identifying CVD in patients with NAFLD. The most significant clinical and lifestyle variables observed by the predictive modeling were age (59 years [54.00–63.00 years]), hypertension (145 mm Hg [134.0–156.0 mm Hg] and 85 mm Hg [79.00–93.00 mm Hg]), waist circumference (98 cm [95.00–105.00 cm]), and sedentary lifestyle, defined as time spent watching TV >4 h/d. In the genetic data, single‐nucleotide polymorphisms in IL16 and ANKLE1 gene were most significant. Our proposed ensemble‐based integrative machine learning model achieved an area under the curve of 0.849 using the random forest modeling for CVD prediction.ConclusionsWe propose a machine learning algorithm that identifies CVD in patients with NAFLD through integration of significant clinical, lifestyle, and genetic risk factors. These patients with NAFLD at higher risk of CVD should be flagged for screening and aggressive treatment of their cardiometabolic risk factors to prevent cardiovascular morbidity and mortality.