Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer.

BACKGROUND: Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use readily available clinicopathologic variables. METHODS: Patients with prostate adenocarcinoma who underwent surgery were identified in the National Cancer Database. Seven ML models were trained to predict organ-confined (OC) disease, extracapsular extension, seminal vesicle invasion (SVI), and lymph node involvement (LNI). Model performance was measured using area under the curve (AUC) on a holdout testing data set. Clinical utility was evaluated using decision curve analysis (DCA). Performance metrics were confirmed on an external validation data set. RESULTS: The ML-based extreme gradient boosted trees model achieved the best performance with an AUC of 0.744, 0.749, 0.816, 0.811 for the OC, ECE, SVI, and LNI models, respectively. The MSK nomograms achieved an AUC of 0.708, 0.742, 0.806, 0.802 for the OC, ECE, SVI, and LNI models, respectively. These models also performed the best on DCA. Findings were consistent on both a holdout internal validation data set as well as an external validation data set. CONCLUSIONS: Our ML models better predicted pathologic stage relative to existing nomograms at predicting pathologic stage. Accurate prediction of pathologic stage can help oncologists and patients determine optimal definitive treatment options for patients with prostate cancer.

Reappraisal of prognostic factors used in the European Pediatric Soft Tissue Sarcoma Study Group RMS 2005 study for localized rhabdomyosarcoma to optimize risk stratification and generate a prognostic nomogram.

BACKGROUND: The objective of this study was to investigate the role of clinical factors together with FOXO1 fusion status in patients with nonmetastatic rhabdomyosarcoma (RMS) to develop a predictive model for event-free survival and provide a rationale for risk stratification in future trials. METHODS: The authors used data from patients enrolled in the European Pediatric Soft Tissue Sarcoma Study Group (EpSSG) RMS 2005 study (EpSSG RMS 2005; EudraCT number 2005-000217-35). The following baseline variables were considered for the multivariable model: age at diagnosis, sex, histology, primary tumor site, Intergroup Rhabdomyosarcoma Studies group, tumor size, nodal status, and FOXO1 fusion status. Main effects and significant second-order interactions of candidate predictors were included in a multiple Cox proportional hazards regression model. A nomogram was generated for predicting 5-year event-free survival (EFS) probabilities. RESULTS: The EFS and overall survival rates at 5 years were 70.9% (95% confidence interval, 68.6%-73.1%) and 81.0% (95% confidence interval, 78.9%-82.8%), respectively. The multivariable model retained five prognostic factors, including age at diagnosis interacting with tumor size, tumor primary site, Intergroup Rhabdomyosarcoma Studies clinical group, and FOXO1 fusion status. Based on each patient's total score in the nomogram, patients were stratified into four groups. The 5-year EFS rates were 94.1%, 78.4%, 65.2%, and 52.1% in the low-risk, intermediate-risk, high-risk, and very-high-risk groups, respectively, and the corresponding 5-year overall survival rates were 97.2%, 91.5%, 74.3%, and 60.8%, respectively. CONCLUSIONS: The results presented here provide the rationale to modify the EpSSG stratification, with the most significant change represented by the replacement of histology with fusion status. This classification was adopted in the new international trial launched by the EpSSG.

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.

Development and Validation of a Recurrence-Free Survival Prediction Model for Locally Advanced Esophageal Squamous Cell Carcinoma with Neoadjuvant Chemoradiotherapy.

BACKGROUND: A recurrence-free survival (RFS) prediction model was developed and validated for patients with locally advanced esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy (NCRT) in combination with surgery. PATIENTS AND METHODS: We included 282 patients with esophageal squamous cell carcinoma who received neoadjuvant chemoradiotherapy (NCRT) combined with surgery, constructed three models incorporating pathological factors, investigated the discrimination and calibration of each model, and compared the clinical utility of each model using the net reclassification index (NRI) and the integrated discrimination index (IDI). RESULTS: Multivariable analysis showed that pathologic complete response (pCR) and lymph node tumor regression grading (LN-TRG) (p < 0.05) were independent prognostic factors for RFS. LASSO regression screened six correlates of LN-TRG, vascular invasion, nerve invasion, degree of differentiation, platelet grade, and a total diameter of residual cancer in lymph nodes to build model three, which was consistent in terms of efficacy in the training set and validation set. Kaplan-Meier (K-M) curves showed that all three models were able to distinguish well between high- and low-risk groups (p < 0.01). The NRI and IDI showed that the clinical utility of model 2 was slightly better than that of model 1 (p > 0.05), and model 3 was significantly better than that of model 2 (p < 0.05). CONCLUSIONS: Clinical prediction models incorporating LN-TRG factors have high predictive efficacy, can help identify patients at high risk of recurrence after neoadjuvant therapy, and can be used as a supplement to the  AJCC/TNM staging system while offering a scientific rationale for early postoperative intervention.

Prediction of Subsequent Vertebral Fracture After Acute Osteoporotic Fractures from Clinical and Paraspinal Muscle Features.

To construct a nomogram based on clinical factors and paraspinal muscle features to predict vertebral fractures occurring after acute osteoporotic vertebral compression fracture (OVCF). We retrospectively enrolled 307 patients with acute OVCF between January 2013 and August 2022, and performed magnetic resonance imaging of the L3/4 and L4/5 intervertebral discs (IVDs) to estimate the cross-sectional area (CSA) and degree of fatty infiltration (FI) of the paraspinal muscles. We also collected clinical and radiographic data. We used univariable and multivariable Cox proportional hazards models to identify factors that should be included in the predictive nomogram. Post-OVCF vertebral fracture occurred within 3, 12, and 24 months in 33, 69, and 98 out of the 307 patients (10.8%, 22.5%, and 31.9%, respectively). Multivariate analysis revealed that this event was associated with percutaneous vertebroplasty treatment, higher FI at the L3/4 IVD levels of the psoas muscle, and lower relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. Area under the curve values for subsequent vertebral fracture at 3, 12, and 24 months were 0.711, 0.724, and 0.737, respectively, indicating remarkable accuracy of the nomogram. We developed a model for predicting post-OVCF vertebral fracture from diagnostic information about prescribed treatment, FI at the L3/4 IVD levels of the psoas muscle, and relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. This model could facilitate personalized predictions and preventive strategies.

Preoperative Gadoxetic Acid-Enhanced MRI Features for Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma: Creating Nomograms for Risk Assessment.

BACKGROUND: Vessels encapsulating tumor cluster (VETC) and microvascular invasion (MVI) have a synergistic effect on prognosis assessment and treatment selection of hepatocellular carcinoma (HCC). Preoperative noninvasive evaluation of VETC and MVI is important. PURPOSE: To explore the diagnosis value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features for MVI, VETC, and recurrence-free survival (RFS) in HCC. STUDY TYPE: Retrospective. POPULATION: 240 post-surgery patients with 274 pathologically confirmed HCC (allocated to training and validation cohorts with a 7:3 ratio) and available tumor marker data from August 2014 to December 2021. FIELD STRENGTH/SEQUENCE: 3-T, T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT: Three radiologists subjectively reviewed preoperative MRI, evaluated clinical and conventional imaging features associated with MVI+, VETC+, and MVI+/VETC+ HCC. Regression-based nomograms were developed for HCC in the training cohort. Based on the nomograms, the RFS prognostic stratification system was further. Follow-up occurred every 3-6 months. STATISTICAL TESTS: Chi-squared test or Fisher's exact test, Mann-Whitney U-test or t-test, least absolute shrinkage and selection operator-penalized, multivariable logistic regression analyses, receiver operating characteristic analysis, Harrell's concordance index (C-index), Kaplan-Meier plots. Significance level: P < 0.05. RESULTS: In the training group, 44 patients with MVI+ and 74 patients with VETC+ were histologically confirmed. Three nomograms showed good performance in the training (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.892 vs. 0.848 vs. 0.910) and validation (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.839 vs. 0.810 vs. 0.855) cohorts. The median follow-up duration for the training cohort was 43.6 (95% CI, 35.0-52.2) months and 25.8 (95% CI, 16.1-35.6) months for the validation cohort. Patients with either pathologically confirmed or nomogram-estimated MVI, VETC, and MVI+/VETC+ suffered higher risk of recurrence. DATA CONCLUSION: GA-enhanced MRI and clinical variables might assist in preoperative estimation of MVI, VETC, and MVI+/VETC+ in HCC. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

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.

A Nomogram for Predicting Cancer-Associated Venous Thromboembolism in Hospitalized Patients Receiving Chemoradiotherapy for Cancer.

PURPOSE: The aim of this study was to develop a novel nomogram to predict cancer-associated venous thromboembolism (CAT) in hospitalized patients with cancer who receive chemoradiotherapy. METHODS: This was a retrospective cohort study of hospitalized patients with cancer who received chemoradiotherapy between January 2010 and December 2022. Predictive factors for CAT were determined using univariate and multivariate logistic regression analyses, and a risk prediction model based on the nomogram was constructed and validated internally. Nomogram performance was assessed using receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 778 patients were eligible for inclusion in this study. The nomogram incorporated 5 independent risk factors: age, cancer stage, use of nonsteroidal anti-inflammatory drugs, D-dimer levels, and history of diabetes mellitus. The area under the curve (AUC) of the nomogram for the training and validation cohorts was 0.816 and 0.781, respectively, with 95% confidence intervals (CIs) of 0.770-0.861 and 0.703-0.860, respectively. The calibration and DCA curves also displayed good agreement and clinical applicability of the nomogram model. CONCLUSIONS: The incidence of CAT was relatively high among patients with cancer receiving chemoradiotherapy. The nomogram risk model developed in this study has good prediction efficiency and can provide a reference for the clinical evaluation of the risk of adverse outcomes in patients with cancer receiving chemoradiotherapy.

Characterization of the CT-based risk factors for concomitant patellofemoral instability in patients with anterior cruciate ligament injury.

OBJECTIVES: Underestimation of concomitant patellofemoral instability in patients with anterior cruciate ligament (ACL) injury has aroused extensive attention. However, the characteristics of the combined injury is not well recognized. Hence, we aimed to characterize the features of the combined injury, and determine the radiographic risk factors. METHODS: Fifteen radiological parameters were identified after discussion and pilot-tested. Radiographic measurements were compared using the analysis of variance model with Tukey post hoc analysis. A stepwise binomial logistic regression was performed and a nomogram model combining the significant risk factors was created. The model performance was validated by C-index, calibration plot, and decision curve. RESULTS: A total of 204 patients (mean [SD] age, 25.1 [6.7] years; 108 [52.9%] male) were included. The final model was updated through regression analysis using 4 parameters as significant risk factors: lateral femoral condyle ratio (OR (95% CI), 1.194 (1.023 to 1.409)), medial anterior tibial subluxation (mATS) (OR (95% CI), 1.234 (1.065 to 1.446)), medial posterior plateau tibial angle (mPPTA) (OR (95% CI), 1.266 (1.088 to 1.500)), and trochlear depth (OR (95% CI), 0.569 (0.397 to 0.784)). C-index for the nomogram was 0.802 (95% CI, 0.731 to 0.873) and was confirmed to be 0.784 through bootstrapping validation. Calibration plot established a good agreement between prediction and observation. Decision curve analysis showed that if threshold probability was over 10%, using the nomogram adds more benefit than either all or none scheme. CONCLUSIONS: Lateral femoral condyle ratio, mATS, mPPTA, and trochlear depth are strong adverse predictors of patellofemoral instability in patients with ACL injury. CLINICAL RELEVANCE: This study characterizes the radiological features of the combined injury. Patellofemoral instability should be noted when treating ACL injuries. KEY POINTS: • The radiological characteristics of the combined ACL injury and patellofemoral instability is not well recognized. • Lateral femoral condyle ratio, mATS, mPPTA, and trochlear depth are predominant risk factors for patellofemoral instability in patients with ACL injury. • Patellofemoral instability should be noted when treating ACL injuries.

Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study.

OBJECTIVES: To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. METHODS: This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). RESULTS: The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort. CONCLUSIONS: Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. CLINICAL RELEVANCE STATEMENT: By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. KEY POINTS: • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

A bimodal nomogram as an adjunct tool to reduce unnecessary breast biopsy following discordant ultrasonic and mammographic BI-RADS assessment.

OBJECTIVE: To develop a bimodal nomogram to reduce unnecessary biopsies in breast lesions with discordant ultrasound (US) and mammography (MG) Breast Imaging Reporting and Data System (BI-RADS) assessments. METHODS: This retrospective study enrolled 706 women following opportunistic screening or diagnosis with discordant US and MG BI-RADS assessments (where one assessed a lesion as BI-RADS 4 or 5, while the other assessed the same lesion as BI-RADS 0, 2, or 3) from two medical centres between June 2019 and June 2021. Univariable and multivariable logistic regression analyses were used to develop the nomogram. DeLong's and McNemar's tests were used to assess the model's performance. RESULTS: Age, MG features (margin, shape, and density in masses, suspicious calcifications, and architectural distortion), and US features (margin and shape in masses as well as calcifications) were independent risk factors for breast cancer. The nomogram obtained an area under the curve of 0.87 (95% confidence interval (CI), 0.83-0.91), 0.91 (95% CI, 0.87 - 0.96), and 0.92 (95% CI, 0.86-0.98) in the training, internal validation, and external testing samples, respectively, and demonstrated consistency in calibration curves. Coupling the nomogram with US reduced unnecessary biopsies from 74 to 44% and the missed malignancies rate from 13 to 2%. Similarly, coupling with MG reduced missed malignancies from 20 to 6%, and 63% of patients avoided unnecessary biopsies. Interobserver agreement between US and MG increased from - 0.708 (poor agreement) to 0.700 (substantial agreement) with the nomogram. CONCLUSION: When US and MG BI-RADS assessments are discordant, incorporating the nomogram may improve the diagnostic accuracy, avoid unnecessary breast biopsies, and minimise missed diagnoses. CLINICAL RELEVANCE STATEMENT: The nomogram developed in this study could be used as a computer program to assist radiologists with detecting breast cancer and ensuring more precise management and improved treatment decisions for breast lesions with discordant assessments in clinical practice. KEY POINTS: • Coupling the nomogram with US and mammography improves the detection of breast cancers without the risk of unnecessary biopsy or missed malignancies. • The nomogram increases mammography and US interobserver agreement and enhances the consistency of decision-making. • The nomogram has the potential to be a computer program to assist radiologists in identifying breast cancer and making optimal decisions.

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.

Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy.

OBJECTIVES: This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC). MATERIALS AND METHODS: A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods. RESULTS: The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients. CONCLUSION: The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT. CLINICAL RELEVANCE STATEMENT: The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment. KEY POINTS: • The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.

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.

Development of a prediction model for predicting the prevalence of nonalcoholic fatty liver disease in Chinese nurses: the first-year follow data of a web-based ambispective cohort study.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is gradually becoming a huge threat to public health. With complex working characteristics, female nurses had been found with high risk of NAFLD. To develop and validate a prediction model to predict the prevalence of NAFLD based on demographic characteristics, work situation, daily lifestyle and laboratory tests in female nurses. METHODS: This study was a part of the Chinese Nurse Cohort Study (The National Nurse Health Study, NNHS), and data were extracted from the first-year follow data collected from 1st June to 1st September 2021 by questionnaires and physical examination records in a comprehensive tertiary hospital. The questionnaires included demographic characteristics, work situation and daily lifestyle. Logistic regression and a nomogram were used to develop and validate the prediction model. RESULTS: A total of 824 female nurses were included in this study. Living situation, smoking history, monthly night shift, daily sleep time, ALT/AST, FBG, TG, HDL-C, UA, BMI, TBil and Ca were independent risk factors for NAFLD occurance. A prediction model for predicting the prevalence of NAFLD among female nurses was developed and verified in this study. CONCLUSION: Living situation, smoking history, monthly night shift, daily sleep time, ALT/AST, FBG, TG, UA, BMI and Ca were independent predictors, while HDL-C and Tbil were independent protective indicators of NAFLD occurance. The prediction model and nomogram could be applied to predict the prevalence of NAFLD among female nurses, which could be used in health improvement. TRIAL REGISTRATION: This study was a part of the Chinese Nurse Cohort Study (The National Nurse Health Study, NNHS), which was a ambispective cohort study contained past data and registered at Clinicaltrials.gov ( https://clinicaltrials.gov/ct2/show/NCT04572347 ) and the China Cohort Consortium ( http://chinacohort.bjmu.edu.cn/project/102/ ).

Prognostic nomograms for breast cancer with lung metastasis: a SEER-based population study.

BACKGROUND: Lung metastasis is a significant adverse predictor of prognosis in patients with breast cancer. Accurate estimation for the prognosis of patients with lung metastasis and population-based validation for the models are lacking. In the present study, we aimed to establish the nomogram to identify prognostic factors correlated with lung metastases and evaluate individualized survival in patients with lung metastasis based on SEER (Surveillance, Epidemiology, and End Results) database. METHODS: We selected 1197 patients diagnosed with breast cancer with lung metastasis (BCLM) from the SEER database and randomly assigned them to the training group (n = 837) and the testing group (n = 360). Based on univariate and multivariate Cox regression analysis, we evaluated the effects of multiple variables on survival in the training group and constructed a nomogram to predict the 1-, 2-, and 3-year survival probability of patients. The nomogram were verified internally and externally by Concordance index (C-index), Net Reclassification (NRI), Integrated Discrimination Improvement (IDI), Decision Curve Analysis (DCA), and calibration plots. RESULTS: According to the results of multi-factor Cox regression analysis, age, histopathology, grade, marital status, bone metastasis, brain metastasis, liver metastasis, human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), surgery, neoadjuvant therapy and chemotherapy were considered as independent prognostic factors for patients with BCLM. The C-index in the training group was 0.719 and the testing group was 0.695, respectively. The AUC values of the 1-, 2-, and 3-year prognostic nomogram in the training group were 0.798, 0.790 and 0.793, and the corresponding AUC values in the testing group were 0.765, 0.761 and 0.722. The calculation results of IDI and NRI were shown. The nomograms significantly improved the risk reclassification for 1-, 2-, and 3-year overall mortality prediction compared with the AJCC 7th staging system. According to the calibration plot, nomograms showed good consistency between predicted and actual overall survival (OS) values for the patients with BCLM. DCA showed that nomograms had better net benefits at different threshold probabilities at different time points compared with the AJCC 7th staging system. CONCLUSIONS: Nomograms that predicted 1-, 2-, and 3-year OS for patients with BCLM were successfully constructed and validated to help physicians in evaluating the high risk of mortality in breast cancer patients.

Establishment and validation of a nomogram for predicting overall survival of upper-tract urothelial carcinoma with bone metastasis: a population-based study.

BACKGROUND: Bone metastasis (BM) carries a poor prognosis for patients with upper-tract urothelial carcinoma (UTUC). This study aims to identify survival predictors and develop a prognostic nomogram for overall survival (OS) in UTUC patients with BM. METHODS: The Surveillance, Epidemiology, and End Results database was used to select patients with UTUC between 2010 and 2019. The chi-square test was used to assess the baseline differences between the groups. Kaplan-Meier analysis was employed to assess OS. Univariate and multivariate analyses were conducted to identify prognostic factors for nomogram establishment. An independent cohort was used for external validation of the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C-index), area under receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). All statistical analyses were performed using SPSS 23.0 and R software 4.2.2. RESULTS: The mean OS for UTUC patients with BM was 10 months (95% CI: 8.17 to 11.84), with 6-month OS, 1-year OS, and 3-year OS rates of 41%, 21%, and 3%, respectively. Multi-organ metastases (HR = 2.21, 95% CI: 1.66 to 2.95, P < 0.001), surgery (HR = 0.72, 95% CI: 0.56 to 0.91, P = 0.007), and chemotherapy (HR = 0.37, 95% CI: 0.3 to 0.46, P < 0.001) were identified as independent prognostic factors. The C-index was 0.725 for the training cohort and 0.854 for the validation cohort, and all AUC values were > 0.679. The calibration curve and DCA curve showed the accuracy and practicality of the nomogram. CONCLUSIONS: The OS of UTUC patients with BM was poor. Multi-organ metastases was a risk factor for OS, while surgery and chemotherapy were protective factors. Our nomogram was developed and validated to assist clinicians in evaluating the OS of UTUC patients with BM.

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.

Normal values of the fronto-occipital relationship in fetuses between 18 and 40 weeks at two maternal fetal medicine units: Bogotá, Colombia.

OBJECTIVE: To establish nomograms for linear measurements of the frontal and occipital horns of the lateral ventricle and their relationship, in pregnant patients between 18 and 40 weeks of gestation and having attended 2 units of Maternal Fetal Medicine in Bogotá-Colombia. METHODOLOGY: A descriptive cross-sectional study with an analytical component was carried out on pregnant patients who utilized the ultrasound services at 2 Maternal-Fetal Medicine units in Bogotá, between 18 and 40 weeks of pregnancy who underwent measurement. From the anterior and posterior horns of the lateral ventricles, the fronto-occipital ratio was calculated at each gestational week, and nomograms were created for each of these variables. RESULTS: Nine hundred and seventy-eight patients were included in the study. The distance of the frontal horns ranged between 6.9 and 51.6 mm with a mean of 19.1 ± 5.8 mm; that of the occipital horns had a measurement between 8.7 and 53 mm with a mean of 28, 1 ± 8.9 mm; on the other hand, the fronto-occipital ratio (FOR) yielded a mean of 0.365 ± 0.067 (0.136-0.616) without bearing any relation to gestational age. The trend of normal values for the studied population is displayed, plotted in percentile curves and nomograms for each gestational age. CONCLUSION: The measurement of the frontal and occipital horns, and the calculation of the fronto-occipital relationship is technically possible between 18 and 40 weeks, finding that the anterior and posterior horns have a positive linear relationship with gestational age. Contrarily, the FOR does not correlate with the gestational age, it was possible to establish a table of percentiles that allows determining the normal values for these measurements during pregnancy.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

PURPOSE: Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. MATERIAL AND METHODS: A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. RESULTS: The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. CONCLUSION: This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.