Using Nomograms Wisely: Predicting Sentinel Node Positivity in Melanoma.

BACKGROUND: Four externally validated sentinel node biopsy (SNB) prediction nomograms exist for malignant melanoma that each incorporate different clinical and histopathologic variables, which can result in substantially different risk estimations for the same patient. We demonstrate this variability by using hypothetical melanoma cases. METHODS: We compared the MSKCC and MIA calculators. Using a random number generator, 300 hypothetical thin melanoma "patients" were created with varying age, tumor thickness, Clark level, location on the body, ulceration, melanoma subtype, mitosis, and lymphovascular invasion (LVI). The chi-square test was used to detect statistically significant differences in risk estimations between nomograms. Multivariate linear regression was used to determine the most relevant contributing pathologic features in cases where the predictions diverged by > 10%. RESULTS: Of 300 randomly generated cases, 164 were deleted as their clinical scenarios were unlikely. The MSKCC nomogram generally calculated a lower risk than the MIA (p < 0.001). The highest risk score attained for any "patient" using MSKCC calculator was 15% achieved in one of 136 patients (0.7%), whereas using the MIA nomogram, 58 of 136 patients (43%, p < 0.001) had predicted risk >15%. Regression analysis on patients with >10% difference between nomograms revealed LVI (26, p < 0.001), mitosis (14, p < 0.001), and melanoma subtype (8, p < 0.001) were the factors with high coefficients within MIA that were not present in MSKCC. CONCLUSIONS: Nomograms are useful tools when predicting SNB risk but provide risk outputs that are quite sensitive to included predictors.

Can the Briganti 2019 nomogram be modified to predict lymph node metastasis risk in patients with prostate cancer detected with in-bore biopsy?

OBJECTIVES: We aimed to modify the Briganti 2019 nomogram and to test whether it is valid for patients who were diagnosed with prostate cancer through in-bore prostate biopsies. METHODS: Data for 204 patients with positive multiparametric prostate MRI and prostate cancer identified either by mpMRI-cognitive/software fusion or in-bore biopsy and who underwent robot-assisted radical prostatectomy and extended pelvic lymph node dissection between 2012 and 2023 were retrospectively analyzed. The Briganti 2019 nomogram was applied to the mpMRI-cognitive/software fusion biopsy group (142 patients) in the original form, and then, two modifications were tested for the targeted component. Original and modified scores were compared. These modifications were adapted for the in-bore biopsy group (62 patients). The final histopathologic stage was regarded as the gold standard. RESULTS: Nodal metastases were identified in 18/142 (12.6%) of mpMRI-cognitive/software fusion biopsy patients and 8/62 (12.9%) of the in-bore biopsy patients. In the mpMRI-cognitive/software fusion biopsy group, tumor size/core size (%) of targeted biopsy cores and positive core percentage on systematic biopsy were significant parameters for lymph node metastasis based on univariate logistic regression analyses (p < 0.05). With the modifications of these parameters for the in-bore biopsy group, V1 modification of the Briganti 2019 nomogram provided 100% sensitivity and 31.5% specificity (AUC:0.627), while V2 modification provided 75% sensitivity and 46.3% specificity (AUC:0.645). CONCLUSIONS: Briganti 2019 nomogram may be modified by utilizing tumor size/core size (%) for targeted biopsy cores instead of positive core percentage on systematic biopsy or by not taking both parameters into consideration to detect node metastasis risk of patients diagnosed with in-bore biopsies.

Impact of tumor multiplicity on the prognosis of patients with primary renal cell carcinoma: a SEER database analysis.

To compare clinical characteristics and survival outcomes of patients with multiple renal cell carcinoma versus single renal cell carcinoma. Develop a prognostic model for predicting prognosis in patients with multiple tumors and analyze prognostic factors. Patients with primary multiple renal cell carcinoma were selected from the Surveillance, Epidemiology, and End Results database (2004-2015). They were divided into single-tumor and multiple-tumor groups. Survival analysis was conducted using the Kaplan-Meier method and log-rank test. A Cox regression model was used to identify potential prognostic factors. A total of 19,489 renal cell carcinoma cases were included, with 947 in the multiple-tumor group and 18,542 in the single-tumor group. The multiple-tumor group had lower cancer-specific survival (P = 0.03, HR = 1.431). Cox regression identified risk factors for the multiple-tumor group including number of tumors, gender, combined summary stage, T stage, N stage, tumor size, and type of surgery. The predicted probabilities showed acceptable agreement with the actual observations at 3-, 5-, and 8-years area under the curve values in both the training and validation cohorts (0.831 vs. 0.605; 0.775 vs. 0.672; and 0.797 vs. 0.699, respectively). Compared with single renal cell carcinoma, multiple renal cell carcinoma is associated with decreased cancer-specific survival. Additionally, we identified several prognostic factors including the number of tumors, T stage, tumor size, and type of surgery. These findings offer valuable insights for selecting appropriate treatment strategies for patients diagnosed with multiple renal cell carcinomas.

Establishment and Validation of Risk Prediction Models for Postoperative Pain After Endoscopic Submucosal Dissection: A Retrospective Clinical Study.

Objective: Postoperative pain is a common complication in endoscopic submucosal dissection (ESD) patients. This study aimed to develop and validate predictive models for postoperative pain associated ESD. Methods: We retrospectively constructed a development cohort comprising 2162 patients who underwent ESD at our hospital between January 2015 and April 2022. The dataset was randomly divided into a training set (n = 1541) and a validation set (n = 621) in a 7:3 ratio. The bidirectional stepwise regression with Akaike's information criterion (AIC) and multivariate logistic regression analysis were used to screen the predictors of post-ESD pain and construct three nomograms. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, Hosmer-Lemeshow (HL) goodness-of-fit test and decision curve analysis (DCA) in internal validation. Results: The proportion of patients developing postoperative pain in the training and testing data set was 25.6% and 28.5%, respectively. Three nomograms were constructed according to the final logistic regression models. The clinical prediction models for preoperative risks, preoperative and intraoperative risks, and perioperative risks consisted of seven, nine and six independent predictors, respectively, after bidirectional stepwise elimination. The models demonstrated the AUC of 0.794 (95% CI 0.768-0.820), 0.823 (95% CI 0.799-0.847) and 0.817 (95% CI 0.792-0.842) in the training cohort and 0.702 (95% CI 0.655-0.748), 0.705 (95% CI 0.659-0.752) and 0.747 (95% CI 0.703-0.790) in the validation cohort. The calibration plot, HL and DCA demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated three robust nomogram models, which might identify patients at risk of post-ESD pain and promising for clinical applications.

Gender-specific prognosis models reveal differences in subarachnoid hemorrhage patients between sexes.

BACKGROUND: Subarachnoid hemorrhage (SAH) represents a severe stroke subtype. Our study aims to develop gender-specific prognostic prediction models derived from distinct prognostic factors observed among different-gender patients. METHODS: Inclusion comprised SAH-diagnosed patients from January 2014 to March 2016 in our institution. Collected data encompassed patients' demographics, admission severity, treatments, imaging findings, and complications. Three-month post-discharge prognoses were obtained via follow-ups. Analyses assessed gender-based differences in patient information. Key factors underwent subgroup analysis, followed by univariate and multivariate analyses to identify gender-specific prognostic factors and establish/validate gender-specific prognostic models. RESULTS: A total of 929 patients, with a median age of 57 (16) years, were analyzed; 372 (40%) were male, and 557 (60%) were female. Differences in age, smoking history, hypertension, aneurysm presence, and treatment interventions existed between genders (p < 0.01), yet no disparity in prognosis was noted. Subgroup analysis explored hypertension history, aneurysm presence, and treatment impact, revealing gender-specific variations in these factors' influence on the disease. Screening identified independent prognostic factors: age, SEBES score, admission GCS score, and complications for males; and age, admission GCS score, intraventricular hemorrhage, treatment interventions, symptomatic vasospasm, hydrocephalus, delayed cerebral ischemia, and seizures for females. Evaluation and validation of gender-specific models yielded an AUC of 0.916 (95% CI: 0.878-0.954) for males and 0.914 (95% CI: 0.885-0.944) for females in the ROC curve. Gender-specific prognostic models didn't significantly differ from the overall population-based model (model 3) but exhibited robust discriminative ability and clinical utility. CONCLUSION: Variations in baseline and treatment-related factors among genders contribute partly to gender-based prognosis differences. Independent prognostic factors vary by gender. Gender-specific prognostic models exhibit favorable prognostic performance.

Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study.

BACKGROUND: To retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases. METHODS: A total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts. RESULTS: Based on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P < 0.001) were independent risk factors for liver metastasis in patients with gastric cancer, and Nomogram was established based on this analysis (C-statistics = 0.911, 95%CI 0.880-0.958), and the C-statistics of the external validation cohorts achieved 0.926. ROC analysis and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic value than single variety. CONCLUSIONS: By innovatively introducing a new tumor location classification method, systemic inflammatory response indicators such as NLR and PLR, and nutritional index OPNI, the risk factors of gastric cancer liver metastasis were determined and a predictive Nomogram model was established, which can provide clinical prediction for patients with gastric cancer liver metastasis.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml.

OBJECTIVES: To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy. PATIENTS AND METHODS: Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit. RESULTS: A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively). CONCLUSION: The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

Validation of stone-kidney size score to predict outcome and complications of pediatric percutaneous nephrolithotomy.

INTRODUCTION: The prevelance of urinary system stone disease in children is emphasizing the need for minimally invasive treatments to decrease morbidity and recurrence risk. Percutaneous nephrolithotomy (PCNL) has emerged as a preferred approach for pediatric patients with complex stones due to its minimally invasive nature, including miniaturized and vacuum-assisted access sheaths, advanced laser technology and tubeless and outpatient procedures. However, adult scoring systems have proven ineffective in predicting success and complications in pediatric PCNL. This highlights the need for specialized scoring systems, such as the Stone-Kidney Size (SKS) scoring system, tailored to pediatric patients and will be evaluated in our study for its association with the stone-free rate (SFR) and complications. MATERIALS AND METHODS: The data of 144 patients aged <17 years who had undergone PCNL between January 2008 and December 2019 were evaluated retrospectively. Demographics, stone characteristics, perioperative/postoperative outcomes were recorded for each patient. The SKS scoring system comprises the stone kidney index (SKI) and the number of stones, assigns one or two points based on single or multiple stones and an SKI value of <0.3 or ≥0.3, respectively. The SKI is computed by dividing the stone's longest axis by the kidney's longest axis. Residual stones less than 4 mm on non-contrast computed tomography are considered clinically insignificant residual fragments (CIRFs). Stone-free and CIRF patients were considered successful results. The relationship between the SKS scoring system and SFR, success, and complication rates after surgery was investigated. Statistical analyses were conducted using SPSS 22.0 software. RESULTS: The SFR was 67.36% and 74.31% when CIRF patients were included, respectively, with a complication rate of 27%. In multivariate analysis, stone treatment history, stone burden, and SKS score were statistically significantly associated with SFR (p < 0.001, p = 0.032, p < 0.001, respectively). Furthermore, the SKS score was the only variable that showed a statistically significant relationship with success. No significant association was found between SKS score and complications (p = 0.342). DISCUSSION: Our study demonstrates a relationship between the SKS scoring system and SFR in pediatric PCNL patients. However, shortcomings have been observed in its capacity to accurately predict post-PCNL complications. Despite being a retrospective analysis and having a single-center design, our study externally validates the relationship between the SKS scoring system and SFR after pediatric PCNL. CONCLUSIONS: The SKS scoring system is associated with SFR in pediatric patients undergoing PCNL; however, this relationship has not been established for complications.

Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19.

Introduction: Patients undergoing maintenance hemodialysis are vulnerable to coronavirus disease 2019 (COVID-19), exhibiting a high risk of hospitalization and mortality. Thus, early identification and intervention are important to prevent disease progression in these patients. Methods: This was a two-center retrospective observational study of patients on hemodialysis diagnosed with COVID-19 at the Lingang and Xuhui campuses of Shanghai Sixth People's Hospital. Patients were randomized into the training (130) and validation cohorts (54), while 59 additional patients served as an independent external validation cohort. Artificial intelligence-based parameters of chest computed tomography (CT) were quantified, and a nomogram for patient outcomes at 14 and 28 days was created by screening quantitative CT measures, clinical data, and laboratory examination items, using univariate and multivariate Cox regression models. Results: The median dialysis duration was 48 (interquartile range, 24-96) months. Age, diabetes mellitus, serum phosphorus level, lymphocyte count, and chest CT score were identified as independent prognostic indicators and included in the nomogram. The concordance index values were 0.865, 0.914, and 0.885 in the training, internal validation, and external validation cohorts, respectively. Calibration plots showed good agreement between the expected and actual outcomes. Conclusion: This is the first study in which a reliable nomogram was developed to predict short-term outcomes and survival probabilities in patients with COVID-19 on hemodialysis. This model may be helpful to clinicians in treating COVID-19, managing serum phosphorus, and adjusting the dialysis strategies for these vulnerable patients to prevent disease progression in the context of COVID-19 and continuous emergence of novel viruses.

A personalized prognostic model for long-term survival in patients with intrahepatic cholangiocarcinoma: a retrospective cohort study.

Purpose: This study aimed to determine the optimal cutoff points for age and tumor size of patients with intrahepatic cholangiocarcinoma (ICC) and to establish and verify a predictive nomogram of overall survival at 1, 3, and 5 years. Methods: From the SEER (Surveillance, Epidemiology, and End Results) database, 1,325 ICC patients were selected and randomly divided into training and testing cohorts at a 7:3 ratio. Using the X-tile software, age and tumor size were classified into 3 subgroups: ≤61, 62-74, and ≥75 years and ≤35, 36-55, and ≥56 mm. Subsequently, univariate and multivariate Cox regression analyses were performed using the R software in the training cohort to determine independent risk factors, compile the prediction nomogram, and verify it with the testing cohort findings. Results: The C-indexes of the new prediction nomograms in the training and testing cohorts were 0.738 (95% confidence interval [CI], 0.718-0.758) and 0.750 (95% CI, 0.72-0.78), respectively. Furthermore, the areas under the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves based on the nomogram were 0.792, 0.853, and 0.838, respectively, higher than the ROC based on the 7th and 8th editions of the American Joint Cancer Commission (AJCC) staging system. Conclusion: This study established and verified a prognostic nomogram that improved the accuracy of the 1-, 3-, and 5-year survival predictions for ICC patients, compared with that based on the 7th and 8th editions of the AJCC staging system, and can help clinicians make personalized survival predictions.

Nomogram for predicting pathologic complete response following preoperative chemoradiotherapy in patients with esophageal squamous cell carcinoma.

Background: In patients with esophageal squamous cell carcinoma (ESCC), accurately predicting a pathologic complete response (pCR) to preoperative chemoradiotherapy (PCRT) has the potential to enable an active surveillance strategy without esophagectomy. We aimed to establish a reliable multiparameter nomogram model that combines tumor characteristics, imaging modalities, and hematologic markers to predict pCR in patients with ESCC who underwent PCRT and esophagectomy. Methods: We retrospectively reviewed the medical records of 457 patients with ESCC who received PCRT followed by esophagectomy between January 2005 and October 2020. The nomogram model was developed using logistic regression analysis with a training cohort and externally validated with a validation cohort. Results: In the training and validation cohorts, 44.2% (126/285) and 48.3% (83/172) of patients, respectively, achieved pCR after PCRT. The 5-year rates of overall survival, progression-free survival, and freedom from local progression in the training cohort were 51.6%, 48.5%, and 77.6%, respectively. The parameters included in the nomogram were histologic grade, clinical N stage, maximum standardized uptake value on positron emission tomography, and post-PCRT biopsy. Hematologic markers were significantly associated with survival outcomes but not with pCR. The area under the receiver operating characteristic curve of the nomogram was 0.717, 0.704, and 0.707 for the training cohort, internal validation cohort, and external validation cohort, respectively. Conclusion: Our nomogram model based on four parameters obtained from standard clinical practice demonstrated good performance in both the training and validation cohorts and could be useful to aid clinical decision-making to determine whether surgery or active surveillance strategy should be pursued.

A novel predictive model for optimizing diabetes screening in older adults.

INTRODUCTION: The fasting blood glucose test is widely used for diabetes screening. However, it may fail to detect early-stage diabetes characterized by elevated postprandial glucose levels. Hence, we developed and internally validated a nomogram to predict the diabetes risk in older adults with normal fasting glucose levels. MATERIALS AND METHODS: This study enrolled 2,235 older adults, dividing them into a Training Set (n = 1,564) and a Validation Set (n = 671) based on a 7:3 ratio. We employed the least absolute shrinkage and selection operator regression to identify predictors for constructing the nomogram. Calibration and discrimination were employed to assess the nomogram's performance, while its clinical utility was evaluated through decision curve analysis. RESULTS: Nine key variables were identified as significant factors: age, gender, body mass index, fasting blood glucose, triglycerides, alanine aminotransferase, the ratio of alanine aminotransferase to aspartate aminotransferase, blood urea nitrogen, and hemoglobin. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.824 in the Training Set and 0.809 in the Validation Set. Calibration curves for both sets confirmed the model's accuracy in estimating the actual diabetes risk. Decision curve analysis highlighted the model's clinical utility. CONCLUSIONS: We provided a dynamic nomogram for identifying older adults at risk of diabetes, potentially enhancing the efficiency of diabetes screening in primary healthcare units.

Ultrafast sequence-based prediction model and nomogram to differentiate additional suspicious lesions on preoperative breast MRI.

OBJECTIVES: To investigate whether ultrafast sequence improves the diagnostic performance of conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating additional suspicious lesions (ASLs) on preoperative breast MRI. MATERIALS AND METHODS: A retrospective database search identified 668 consecutive patients who underwent preoperative breast DCE-MRI with ultrafast sequence between June 2020 and July 2021. Among these, 107 ASLs from 98 patients with breast cancer (36 multifocal, 42 multicentric, and 29 contralateral) were identified. Clinical, pathological, conventional MRI findings, and ultrafast sequence-derived parameters were collected. A prediction model that adds ultrafast sequence-derived parameters to clinical, pathological, and conventional MRI findings was developed and validated internally. Decision curve analysis and net reclassification index statistics were performed. A nomogram was constructed. RESULTS: The ultrafast model adding time to peak enhancement, time to enhancement, and maximum slope showed a significantly increased area under the receiver operating characteristic curve compared with the conventional model which includes age, human epidermal growth factor receptor 2 expression of index cancer, size of index cancer, lesion type of index cancer, location of ASL, and size of ASL (0.92 vs. 0.82; p = 0.002). The decision curve analysis showed that the ultrafast model had a higher overall net benefit than the conventional model. The net reclassification index of ultrafast model was 23.3% (p = 0.001). CONCLUSION: A combination of ultrafast sequence-derived parameters with clinical, pathological, and conventional MRI findings can aid in the differentiation of ASL on preoperative breast MRI. CLINICAL RELEVANCE STATEMENT: Our prediction model and nomogram that was based on ultrafast sequence-derived parameters could help radiologists differentiate ASLs on preoperative breast MRI. KEY POINTS: Ultrafast MRI can diminish background parenchymal enhancement and possibly improve diagnostic accuracy for additional suspicious lesions (ASLs). Location of ASL, larger size of ASL, and higher maximum slope were associated with malignant ASL. The ultrafast model and nomogram can help preoperatively differentiate additional malignancies.

[Predicting the 3-year tumor-specific survival in patients with T3a non-metastatic renal cell carcinoma].

OBJECTIVE: To predict the 3-year cancer-specific survival (CSS) of patients with non-metastatic T3a renal cell carcinoma after surgery. METHODS: A total of 336 patients with pathologically confirmed T3a N0-1M0 renal cell carcinoma (RCC) who underwent surgical treatment at the Department of Urology, Peking University Third Hospital from March 2013 to February 2021 were retrospectively collected. The patients were randomly divided into a training cohort of 268 cases and an internal validation cohort of 68 cases at an 4 ∶ 1 ratio. Using two-way Lasso regression, variables were selected to construct a nomogram for predicting the 3-year cancer-specific survival (CSS) of the patients with T3aN0-1M0 RCC. Performance assessment of the nomogram included evaluation of discrimination and calibration ability, as well as clinical utility using measures such as the concordance index (C-index), time-dependent area under the receiver operating characteristic curve [time-dependent area under the curve (AUC)], calibration curve, and decision curve analysis (DCA). Risk stratification was determined based on the nomogram scores, and Kaplan-Meier survival analysis and Log-rank tests were employed to compare progression-free survival (PFS) and cancer-specific survival (CSS) among the patients in the different risk groups. RESULTS: Based on the Lasso regression screening results, the nomogram was constructed with five variables: tumor maximum diameter, histological grading, sarcomatoid differentiation, T3a feature, and lymph node metastasis. The baseline data of the training and validation sets showed no statistical differences (P>0.05). The consistency indices of the column diagram were found to be 0.808 (0.708- 0.907) and 0.903 (0.838-0.969) for the training and internal validation sets, respectively. The AUC values for 3-year cancer-specific survival were 0.843 (0.725-0.961) and 0.923 (0.844-1.002) for the two sets. Calibration curves of both sets demonstrated a high level of consistency between the actual CSS and predicted probability. The decision curve analysis (DCA) curves indicated that the column diagram had a favorable net benefit in clinical practice. A total of 336 patients were included in the study, with 35 cancer-specific deaths and 69 postoperative recurrences. According to the line chart, the patients were divided into low-risk group (scoring 0-117) and high-risk group (scoring 119-284). Within the low-risk group, there were 16 tumor-specific deaths out of 282 cases and 36 postoperative recurrences out of 282 cases. In the high-risk group, there were 19 tumor-specific deaths out of 54 cases and 33 post-operative recurrences out of 54 cases. There were significant differences in progression-free survival (PFS) and cancer-specific survival (CSS) between the low-risk and high-risk groups (P < 0.000 1). CONCLUSION: A nomogram model predicting the 3-year CSS of non-metastatic T3a renal cell carcinoma patients was successfully constructed and validated in this study. This nomogram can assist clinicians in accurately assessing the long-term prognosis of such patients.

Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.

OBJECTIVES: Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS. MATERIAL AND METHODS: One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis. RESULTS: Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001). CONCLUSIONS: Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment. CLINICAL RELEVANCE STATEMENT: For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure. KEY POINTS: The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.

[Analysis of clinical and genetic characteristics of the severe liver disease phenotype in patients with hepatolenticular degeneration].

Objective: To investigate the clinical and genetic characteristics and predictive role of the severe liver disease phenotype in patients with hepatolenticular degeneration (HLD). Methods: Inpatients with HLD confirmed at Xinhua Hospital affiliated with Shanghai Jiao Tong University School of Medicine from January 1989 to December 2022 were selected as the research subjects. Clinical classification was performed according to the affected organs. Patients with liver disease phenotypes were classified into the liver disease group and further divided into the severe liver disease group and the ordinary liver disease group. The clinical characteristics and genetic variations were compared in each group of patients. The predictive indicators of patients with severe liver disease were analyzed by multiple regression. Statistical analysis was performed using the t-test, Mann-Whitney U test, or χ(2) test according to different data. Results: Of the 159 HLD cases, 142 were in the liver disease group (34 in the severe liver disease group and 108 in the ordinary liver disease group), and 17 were in the encephalopathy group. The median age of onset was statistically significantly different between the liver disease group and the encephalopathy group [12.6 (7.0, 13.3) years versus 16.9 (11.0, 21.5) years, P<0.01]. 156 ATP7B gene mutation sites were found in 83 cases with genetic testing results, of which 54 cases carried the p.Arg778Leu gene mutation (allele frequency 46.2%). Compared with patients with other types of gene mutations (n=65), patients with homozygous p.Arg778Leu mutations (n=18) had lower blood ceruloplasmin and albumin levels, a higher prognostic index, Child-Pugh score, an international normalized ratio, and prothrombin time (P<0.05). Hemolytic anemia, corneal K-F ring, homozygous p.Arg778Leu mutation, and multiple laboratory indexes in the severe liver disease group were statistically significantly different from those in the ordinary liver disease group (P<0.05). Multivariate logistic regression analysis showed that the predictive factors for severe liver disease were homozygous p.Arg778Leu mutation, total bilirubin, and bile acids (ORs=16.512, 1.022, 1.021, 95% CI: 1.204-226.425, 1.005-1.039, and 1.006-1.037, respectively, P<0.05). The drawn ROC curve demonstrated a cutoff value of 0.215 3, an AUC of 0.953 2, and sensitivity and specificity of 90.91% and 92.42%, respectively. Conclusion: Liver disease phenotypes are common in HLD patients and have an early onset. Total bilirubin, bile acids, and the homozygous p.Arg778Leu mutation of ATP7B is related to the severity of liver disease in HLD patients, which aids in predicting the occurrence and risk of severe liver disease.

Prognostic Evaluation and Survival Prediction for Combined Hepatocellular-Cholangiocarcinoma Following Hepatectomy.

Purpose: This study aimed to assess prognostic factors associated with combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and to predict 5-year survival based on these factors. Materials and Methods: Patients who underwent definitive hepatectomy from 2006 to 2022 at a single institution was retrospectively analyzed. Inclusion criteria involved a pathologically confirmed diagnosis of cHCC-CCA. Results: A total of 80 patients with diagnosed cHCC-CCA were included in the analysis. The median progression-free survival (PFS) was 15.6 months, while distant metastasis-free survival (DMFS), hepatic progression-free survival (HPFS), and overall survival (OS) were 50.8, 21.5, and 85.1 months, respectively. In 52 cases of recurrence, intrahepatic recurrence was the most common initial recurrence (34/52), with distant metastasis in 17 cases. Factors associated with poor DMFS included tumor necrosis, lymphovascular invasion (LVI), perineural invasion and histologic compact type. Postoperative CA19-9, tumor necrosis, LVI, and close/positive margin were associated with poor overall survival. LVI emerged as a key factor affecting both DMFS and OS, with a 5-year OS of 93.3% for patients without LVI compared to 35.8% with LVI. Based on these factors, a nomogram predicting 3-year and 5-year DMFS and OS was developed, demonstrating high concordance with actual survival in the cohort (Harrell C-index 0.809 for OS, 0.801 for DMFS, respectively). Conclusion: The prognosis of cHCC-CCA is notably poor when combined with lymphovascular invasion. Given the significant impact of adverse features, accurate outcome prediction is crucial. Moreover, consideration of adjuvant therapy may be warranted for patients exhibiting poor survival and increased risk of local recurrence or distant metastasis.

Risk factors of acute ischemic stroke and the role of angiotensin I in predicting prognosis of patients undergoing endovascular thrombectomy.

Purpose: The interaction between the renin-angiotensin system (RAS) and the acute ischemic stroke (AIS) is definite but not fully understood. This study aimed to analyze the risk factors of AIS and explore the role of serum indicators such as angiotensin I (Ang I) in the prognosis of patients undergoing endovascular thrombectomy (EVT). Patients and methods: Patients with AIS who underwent EVT and healthy controls were retrospectively enrolled in this study, and the patients were divided into a good or a poor prognosis group. We compared Ang I, blood routine indexes, biochemical indexes, electrolyte indexes, and coagulation indexes between patients and controls. We used univariate and multivariate logistic regression analyses to evaluate possible risk factors for AIS and the prognosis of patients undergoing EVT. Independent risk factors for the prognosis of patients undergoing EVT were identified through multifactorial logistic regression analyses to construct diagnostic nomograms, further assessed by receiver operating characteristic curves (ROC). Results: Consistent with previous studies, advanced age, high blood glucose, high D-dimer, and high prothrombin activity are risk factors for AIS. In addition, Ang I levels are lower in AIS compared to the controls. The level of Ang I was higher in the good prognosis group. Furthermore, we developed a nomogram to evaluate its ability to predict the prognosis of AIS after EVT. The AUC value of the combined ROC model (Ang I and albumin-globulin ratio (AGR)) was 0.859. Conclusions: In conclusion, advanced age, high blood glucose, high D-dimer, and high prothrombin activity are risk factors for AIS. The combined Ang I and AGR model has a good predictive ability for the prognosis of AIS patients undergoing arterial thrombectomy.

Develop a bone mineral density T-score distribution nomograms based on osteoporosis risk factors for middle-aged and older adults.

PURPOSE: This study aimed to understand how age, health status, and lifestyle impact bone mineral density (BMD) in middle-aged and older adults, focusing on predicting osteoporosis risk. METHODS: This study included 2836 participants aged 50-88 from the Health Improvement Program of Bone (HOPE) conducted from 2021 to 2023. We used logistic regression to make a prediction tool. Then checked its accuracy and reliability using receiver operating characteristic (ROC) and calibration curves. RESULTS: Factors like age, body weight, prior fractures, and smoking were independently found to affect BMD T-score distribution in men. In women, age and body weight were identified as independent factors influencing BMD T-score distribution. A nomogram was created to visually illustrate these predictive relationships. CONCLUSIONS: The nomogram proved highly accurate in identifying men aged 50 and above and postmenopausal women based on their BMD T-score distribution, improving clinical decision-making and patient care in osteoporosis evaluation and treatment.

Can a nomogram predict apical prostate cancer pathology upgrade from fusion biopsy to final pathology? A multicenter study.

BACKGROUND: This study evaluates the efficacy of a nomogram for predicting the pathology upgrade of apical prostate cancer (PCa). METHODS: A total of 754 eligible patients were diagnosed with apical PCa through combined systematic and magnetic resonance imaging (MRI)-targeted prostate biopsy followed by radical prostatectomy (RP) were retrospectively identified from two hospitals (training: 754, internal validation: 182, internal-external validation: 148). A nomogram for the identification of apical tumors in high-risk pathology upgrades through comparing the results of biopsy and RP was established incorporating statistically significant risk factors based on univariable and multivariable logistic regression. The nomogram's performance was assessed via the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). RESULTS: Univariable and multivariable analysis identified age, targeted biopsy, number of targeted cores, TNM stage, and the prostate imaging-reporting and data system score as significant predictors of apical tumor pathological progression. Our nomogram, based on these variables, demonstrated ROC curves for pathology upgrade with values of 0.883 (95% CI, 0.847-0.929), 0.865 (95% CI, 0.790-0.945), and 0.840 (95% CI, 0.742-0.904) for the training, internal validation and internal-external validation cohorts respectively. Calibration curves showed good consistency between the predicted and actual outcomes. The validation groups also showed great generalizability with the calibration curves. DCA results also demonstrated excellent performance for our nomogram with positive benefit across a threshold probability range of 0-0.9 for the training and internal validation group, and 0-0.6 for the internal-external validation group. CONCLUSION: The nomogram, integrating clinical, radiological, and pathological data, effectively predicts the risk of pathology upgrade in apical PCa tumors. It holds significant potential to guide clinicians in optimizing the surgical management of these patients.