Training Cohort Research Articles

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy.

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Abstract PO1-07-07: Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving treatment

Abstract Background Despite modern surgical and irradiation techniques, ipsilateral breast tumor recurrence (IBTR) accounts for 5-15% of all cancer recurrences in women treated with breast conservative treatment. Historically, the methods to define true recurrence (TR) and new primary tumor (NP) for IBTR mainly rely on clinical and pathological criteria, limiting the accuracy of the discerption and causing misclassification. This study aimed to develop a preoperative, noninvasive model for distinguishing IBTR by integrating clinicopathological review with paired dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) at diagnosis and following in-breast recurrence. MethodsWe retrospectively extracted radiomics features from MRI to develop a radiomic cohort of IBTR (n =46), among which all patients underwent paired preoperative DCE-MRI. This radiomic cohort was divided into a training cohort (n =27) and a validation cohort (n =19) with stratified random sampling. Classification of IBTR as TR or NP on the basis of tumor location, histologic subtype, estrogen receptor, and HER2 status was set as the gold standard. The least absolute shrinkage and selection operator (LASSO) regression and logistic regression were utilized to perform radiomics feature selection and model training, respectively. The clinical utility of the model was determined via decision curve analysis (DCA). Results We selected three radiomics features (first-order feature Kurtosis from the first postcontrast phase on IBTR DCE-MRI, GLCM feature Imc2 from the first postcontrast phase on IBTR DCE-MRI, and the delta feature Correlation from the precontrast phase of the change between IBTR and primary tumor DCE-MRI) to develop an IBTR-classification predicting radiomic model, which performed well in the validation cohort (AUC 0.867, 95% confidence interval (CI) 0.694-1). Further investigation for sensitivity (73.3%) and specificity (100%) verified a favorable concordance between the radiomic classification and the conventional standard, with a diagnostic accuracy of 79%. Conclusions: Our study demonstrated the feasibility of the radiomics model in predicting IBTR classification and provided preoperative information about the nature of “recurrence”. This might have important implications in surgical approaches and multidisciplinary care for IBTR. Further efforts are needed to improve the reproducibility of radiomics features and models in multiple centers. Citation Format: Feilin Qu, Guan-Hua Su, Jin-Hui Li, Chao You, Zhi-Ming Shao. Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-07.

Abstract PO4-02-12: MDA iLobular Prognostic Tool: A Novel Approach for Risk Stratification in Invasive Lobular Carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is a distinct subtype of breast cancer that differs from the more common invasive ductal carcinoma (IDC) by its unique characteristics that influence prognosis. Recent studies have shown that ILC is associated with worse long-term outcomes compared to IDC. Risk stratification is essential in breast cancer for tailoring treatment plans to the individual patient’s needs, maximizing the chances of successful outcomes and minimizing unnecessary interventions. Although there are multiple tools available for breast cancer, there is currently not a risk stratification tool that is specific for ILC. The MDA iLobular prognostic tool is the first risk stratification tool that has been developed specifically for ILC patients. Methods: We retrospectively searched for patients treated at MD Anderson Cancer Center with a diagnosis of stage I-III ILC in our prospectively collected electronic database. The study focused on two primary endpoints: Overall Survival (OS) and Distant Recurrence Free Survival (DRFS). We utilized univariate and multivariate Cox Proportional Hazard (PH) regression models to assess the statistical significance of all variables. The univariate Cox analysis identified prognostic factors, which were further analyzed using backward and stepwise multivariate Cox proportional hazards regression analysis. This process helped identify statistically significant prognostic factors that were included in the final multivariate models. We estimated hazard ratios and 95% confidence intervals for each factor, considering a P-value of < 0.05 as statistically significant. To evaluate the performance of the fitted multivariate Cox PH regression models for OS and DRFS, we randomly divided two-thirds of the data points into a training dataset, while the remaining one-third constituted the test dataset. We assessed the discrimination capacity of each model using Harrell's C-index. Results: The study included a total of 4,216 female patients, with a median age of 56 years. The median pathological tumor size was 2 mm, and the median number of lymph nodes was one. Among these patients, 1,376 were pre-menopausal, while 2,837 were post-menopausal. The training cohort was a subset of 2,950 patients and the test cohort was a subset of 1,266 patients. After evaluating various prognostic models, we identified the model with the highest prognostic accuracy for OS and DRFS. This selected model demonstrated a Harrell's C-index of 0.704 for OS and 0.718 for DRFS on the training dataset and a Harrell's C-index of 0.702 for OS and 0.671 for DRFS on the test dataset. The model incorporated several covariates, including age at the time of diagnosis, number of lymph nodes, pathological tumor size (mm), ER status, tumor grade, ILC histology, and the presence or absence of concomitant LCIS (Table 1). Conclusion: In conclusion, the MDA iLobular prognostic tool represents a significant advancement as the first dedicated tool for risk stratifying ILC, providing valuable guidance for tailoring therapy to individual ILC patients based on their specific risk profiles. Table 1. Multivariate Cox proportional hazard model parameter estimates for OS and DRFS Citation Format: Jason Mouabbi, Sarah Pasyar, Roland Bassett, Akshara Singareeka Raghavendra, Amy Hassan, Rachel Layman, Debu Tripathy. MDA iLobular Prognostic Tool: A Novel Approach for Risk Stratification in Invasive Lobular Carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-02-12.

Abstract PO5-15-10: Plasticity marker FOXC1 expression accurately predicts efficacy of Adjuvant Tamoxifen + Chemotherapy in reducing all-cause mortality in ER+LN- Breast Cancer: Validation in the SCAN-B Prospective Study (NCT02306096)

Abstract INTRODUCTION: Despite the development and validation of multimarker gene panels like OncotypeDx®, Mammaprint® and EndoPredict®, an affordable and globally accessible biomarker approach that can predict increased risk of all-cause mortality in patients diagnosed with ER+LN- breast cancer, as well as the efficacy of adjuvant endocrine + chemotherapy in mitigating such elevated risk, continues to be an unmet medical need. Lack of such a pragmatic solution continues to be the cause of preventable recurrent/metastatic disease in patients diagnosed with ER+LN- breast cancer in resource-challenged settings around the world. We hypothesized that a predictive biomarker strategy that measures expression of the plasticity marker FOXC1, in combination with clinical parameters like tumor size (TS) and tumor grade (TG), may offer equivalent results to that of the above multimarker gene panels at a significantly lower economic cost without compromising accuracy of prediction results. METHODS: Pre-treatment tumor RNA data obtained from a training cohort (compendium of gene expression microarray datasets, n=2857) and a single large validation cohort (SCAN-B Prospective Multicenter Observational Study, n=3520) for patients diagnosed with ER+LN- breast cancer were analyzed for FOXC1 expression, tumor size (TS) and tumor grade (TG) and correlated with Recurrence-Free Survival (RFS) and Overall Survival (OS). Optimized biomarker cutoff values based on model area-under-curve were leave-one-out cross validated and Risk-of-Recurrence (ROR) prediction algorithm derived utilizing the (compendium) training dataset. The unmodified strategy was then validated in the independent (SCAN-B) validation dataset. RESULTS: A predetermined High ROR score calculated using FOXC1 expression, TS and TG (trained using the compendium dataset) predicted efficacy of adjuvant endocrine + chemotherapy over that of adjuvant endocrine therapy alone in ER+LN- patients in terms of statistically significant reduction in all-cause mortality at 8-years post-diagnosis in the SCAN-B validation dataset (4.66% vs 24.06%, n=326, OR=6.48, 95%CI [2.97-14.11], p< 0.0001). OncotypeDx® (5.7% vs 14.4%, n=762, OR=2.79, 95%CI [1.37-5.67], p=0.002), Mammaprint® (5.1% vs 18.4%, n=665, OR= 95%CI [2.04-8.43], p< 0.0001) and Endopredict® (4.8% vs 15.2%, n=923, OR=3.58 95%CI [1.78-7.21], p=0.0002) were also statistically significant predictors of the same. CONCLUSION: Pre-treatment tumor FOXC1 mRNA or protein expression (assessed using qRT-PCR or routine immunohistochemistry (IHC), respectively, when combined with TS and TG presents a unique and economical alternative solution to multimarker gene panel tests like OncotypeDx®, Mammaprint® or Endopredict®, for guiding therapy of patients diagnosed with ER+LN- breast cancer in resource challenged settings. Such an approach to identify elevated risk of recurrence in patients diagnosed with ER+LN- breast cancer and prevent the same by guiding adjuvant endocrine + chemotherapy decisions, could help to extend recurrence-free and overall survival. Such an approach merits testing in real world ER+LN- patient cohorts in resource-challenged settings to help support implementation of this FOXC1-driven predictive biomarker strategy in the clinic. Citation Format: Partha Ray, Tania Ray, Clive Taylor, Robert Hussa. Plasticity marker FOXC1 expression accurately predicts efficacy of Adjuvant Tamoxifen + Chemotherapy in reducing all-cause mortality in ER+LN- Breast Cancer: Validation in the SCAN-B Prospective Study (NCT02306096) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-15-10.

Leveraging artificial intelligence to identify high-risk patients for postoperative sore throat: An observational study.

Postoperative sore throat (POST) is a prevalent complication after general anesthesia and targeting high-risk patients helps in its prevention. This study developed and validated a machine learning model to predict POST. A total number of 834 patients who underwent general anesthesia with endotracheal intubation were included in this study. Data from a cohort of 685 patients was used for model development and validation, while a cohort of 149 patients served for external validation. The prediction performance of random forest (RF), neural network (NN), and extreme gradient boosting (XGBoost) models was compared using comprehensive performance metrics. The Local Interpretable Model-Agnostic Explanations (LIME) methods elucidated the best-performing model. POST incidences across training, validation, and testing cohorts were 41.7%, 38.4%, and 36.2%, respectively. Five predictors were age, sex, endotracheal tube cuff pressure, endotracheal tube insertion depth, and the time interval between extubation and the first drinking of water after extubation. After incorporating these variables, the NN model demonstrated superior generalization capabilities in predicting POST when compared to the XGBoost and RF models in external validation, achieving an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% CI 0.74-0.89) and a precision-recall curve (AUPRC) of 0.77 (95% CI 0.66-0.86). The model also showed good calibration and clinical usage values. The NN model outperforms the XGBoost and RF models in predicting POST, with potential applications in the healthcare industry for reducing the incidence of this common postoperative complication.

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.

To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging

Rationale and ObjectivesTo develop and validate a predictive model for osteoporosis and osteopenia prediction by fusing deep transfer learning (DTL) features and classical radiomics features based on single-source dual-energy computed tomography (CT) virtual monochromatic imaging. MethodsA total of 606 lumbar vertebrae with dual-energy CT imaging and quantitative CT (QCT) evaluation were included in the retrospective study and randomly divided into the training (n=424) and validation (n=182) cohorts. Radiomics features and DTL features were extracted from 70-keV monochromatic CT images, followed by feature selection and model construction, radiomics and DTL features models were established. Then, we integrated the selected two types of features into a features fusion model. We developed a two-level classifier for the hierarchical pairwise classification of each vertebra. All the vertebrae were first classified into osteoporosis and non-osteoporosis groups, then non-osteoporosis group was classified into osteopenia and normal groups. QCT was used as reference. The predictive performance and clinical usefulness of three models were evaluated and compared. ResultsThe area under the curve (AUC) of the features fusion, radiomics and DTL models for the classification between osteoporosis and non-osteoporosis were 0.981, 0.999, 0.997 in the training cohort and 0.979, 0.943, 0.848 in the validation cohort. Furthermore, the AUCs of the abovementioned models for the differentiation between osteopenia and normal were 0.994, 0.971, 0.996 in the training cohort and 0.990, 0.968, 0.908 in the validation cohort. The overall accuracy of the abovementioned models for two-level classifications was 0.979, 0.955, 0.908 in the training cohort and 0.918, 0.885, 0.841 in the validation cohort. Decision curve analysis (DCA) showed that all models had high clinical value. ConclusionThe feature fusion model can be used for osteoporosis and osteopenia prediction with improved predictive ability over a radiomics model or a DTL model alone.

Clinical study on forecasting the prognosis of patients with cerebellar hemorrhage based on CT radiomics models

Background and purposeWe created and verified a clinical–radiomics nomogram to forecast the prognosis of patients with cerebellar hemorrhage. Materials and MethodsEighty-eight patients with cerebellar hemorrhage from two different hospitals were retrospectively analyzed (62 cases from first hospital (training cohort) and 27 cases from second hospital (external validation cohort)). All patients underwent computed tomography scanning within 72 hours of the onset of cerebellar hemorrhage and were split into two categories based on their prognosis. Clinically independent risk factors were identified using the independent samples t-test and binary logistic regression analysis. Radiomic features were obtained using the SelectkBest, variance threshold, least absolute shrinkage, and selection operator. Radiomics scores (rad-scores) were produced from eight radiomics features of perihematomal edema (PHE) and hematoma associated with cerebellar hemorrhage prognosis. Logistic regression analyses of the rad-scores against clinical risk factors yielded clinical–radiomics column line plots. Predictive performance of the column line plots was validated using decision curve analysis analyses in both the training and external validation cohorts. ResultsTo construct rad-scores, eight radiomics traits and four clinical risk factors were combined to create clinical–radiomics models. In both the training cohort (area under the curve [AUC], 0.96 versus vs. 0.87/0.84) and the external validation cohort (AUC: 0.93 vs. 0.74/0.69), the clinical–radiomics nomogram achieved superior performance to the clinical and radiomics nomogram models. Clinical–radiomics column line plot demonstrated satisfactory calibration and clinical advantages in both cohorts. ConclusionsRadiomics features of PHE and hematoma can predict cerebellar hemorrhage prognosis. Clinical–radiomics models can help clinicians make individualized clinical treatment decisions and efficacy assessments for patients with cerebellar hemorrhage, help physicians prepare for possible deterioration, and promptly communicate treatment goals and efficacy expectations to patients and their families.

Development and validation of a nomogram for pneumonia risk in burn patients with inhalation injury: a multicenter retrospective cohort study.

Burn patients with inhalation injury are at higher risk of developing pneumonia, and yet there is no reliable tool for the assessment of the risk for such patients at admission. This study aims to establish a predictive model for pneumonia risk for burn patients with inhalation injury based on clinical findings and laboratory tests. This retrospective study enrolled 546 burn patients with inhalation injury. They were grouped into a training cohort and a validation cohort. The least absolute shrinkage and selection operator (LASSO) regression analysis and binary logistic regression analysis were utilized to identify risk factors for pneumonia. Based on the factors, a nomogram for predicting pneumonia in burn patients with inhalation injury was constructed. Areas under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA) were used to evaluate the efficiency of the nomogram in both the training and validation cohorts. The training cohort included 432 patients, and the validation cohort included 114 patients, with a total of 225 (41.2%) patients experiencing pneumonia. Inhalation injury, tracheal intubation/tracheostomy, low serum albumin, and high blood glucose were independent risk factors for pneumonia in burn patients with inhalation injury and they were further used to build the nomogram. The AUC of the nomogram in the training and validation cohorts were 0.938 (95% CI: 0.917-0.960) and 0.966 (95% CI: 0.931-1), respectively. The calibration curve for probability of pneumonia showed optimal agreement between the prediction by nomogram and the actual observation, and the DCA indicated that the constructed nomogram conferred high clinical net benefit. This nomogram can accurately predict the risk of developing pneumonia for burn patients with inhalation injury, and help professionals to identify high-risk patients at an early stage as well as to make informed clinical decisions.

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Background and purposeRecently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). Materials and methodsTotally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. ResultsThe final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. ConclusionThe composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.

Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment. This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts. Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI: 0.790-0.903), 0.845 (95% CI: 0.740-0.919), and 0.852 (95% CI: 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model. The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.

The added value of ultrasound imaging biomarkers to clinicopathological factors for the prediction of high-risk Oncotype DX recurrence scores in patients with breast cancer.

The Oncotype DX (ODX) recurrence score (RS), a 21-gene assay, has been proven to recognize patients at high risk of recurrence (RS ≥26) who would benefit from chemotherapy. However, it has limited availability and high costs. Our study thus aimed to identify ultrasound (US) imaging biomarkers and develop a prediction model for identifying patients with a high ODX RS. In this retrospective study, consecutive patients with T1-3N0-1M0 breast cancer who were hormone receptor positive and human epidermal growth factor receptor 2 (HER2) negative who had an available ODX RS were reviewed. Patients treated from May 2012 and December 2015 were placed into a training cohort, and those treated from January 2016 to January 2017 were placed in a validation cohort. Clinicopathologic data were collected, and preoperative US scans were analyzed. Univariable and multivariable regression analyses were performed to evaluate the independent predictors for a high-risk of breast cancer in the training cohort, and a nomogram was developed and evaluated with the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). A total of 363 patients were in the training cohort and 160 in the validation cohort, with the proportion with a high RS (RS 26-100) being 14% and 13.1%, respectively. Echogenic halo, enhanced posterior echo, low level of progesterone receptor (PR), and high Ki-67 index were identified as independent risk factors for high RS (all P values <0.05). The nomogram was constructed based on the combined model, which showed a better discrimination ability than did the clinicopathological model [combined model: AUC =0.95, 95% confidence interval (CI): 0.93-0.97; clinicopathological model: AUC =0.89, 95% CI: 0.86-0.92; P=0.001] and greater clinical benefit according to DCA. Furthermore, the nomogram was found to be effective in the validation cohort (AUC =0.90, 95% CI: 0.84-0.94), especially in patients with stage T1N0M0 disease (AUC =0.91, 95% CI: 0.84-0.95). US features may serve as valuable imaging biomarkers for the prediction of high recurrence risk in patients with T1-3N0-1M0 breast cancer and hormone receptor (HR)-positive and HER2-negative status. A nomogram incorporating PR status, Ki-67 index, and US imaging biomarkers showed a good discrimination ability in the early selection of patients at high risk of recurrence, especially in those with stage T1N0M0 disease.

Development and validation of [18F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy.

Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer. A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients. The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort. We developed and validated a novel [18F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.

Nomogram Predicts Prognostic Factors for Head and Neck Cutaneous Melanoma: A Population-Based Analysis

BackgroundThe head and neck cutaneous melanoma (HNCM) accounts for 20% of newly diagnosed melanoma. Research on prognostic models for their survival yet remains largely unexplored. This study employed a nomogram approach to develop and validate a predictive model for both overall survival (OS) and disease-specific survival (DSS) in patients with HNCM. MethodsThis study analyzed the HNCM patients diagnosed between 2004 and 2014 from SEER database. To identify independent prognostic factors for HNCM, we integrated results from univariate Cox regression analysis, random survival forests, and LASSO regression with cross-validation. A nomogram was designed and validated based on the identified characteristics to predict the 3-, 5-, and 8-year OS and DSS of patients with HNCM. ResultsAge, Stage, Ulceration, Thickness, Chemotherapy, LN_Metastasis, Radiation were identified as independent prognostic factors. The nomogram achieved a satisfactory performance with C-indices of 0.824(DSS) and 0.757(OS) in the training cohort and 0.827(DSS) and 0.749(OS) in the validation cohort, respectively. The AUC for the OS at 3-, 5-, and 8- years were 0.789, 0.788, and 0.794 for the training cohort, and 0.778, 0.776, and 0.795 for the validation cohort, respectively. For DSS, the AUC at 3-, 5-, and 8- years were 0.859, 0.842, and 0.828 in the training cohort, and 0.864, 0.844, and 0.834 in the validation cohort, respectively. The calibration curve showed that there was a strong correlation between the observed outcomes and the predicted survival probability. ConclusionThis study established and validated predictive nomograms for HNCM patients with robust predictive performance.

Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19

ObjectiveOur aim was to develop and validate a nomogram for predicting the in-hospital 14-day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 (COVID-19). MethodsClinical data of patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from December 2022 to February 2023 and the north campus of Shanghai Ninth People's Hospital from April 2022 to June 2022 were collected. A total of 408 patients from Renmin Hospital of Wuhan University were selected as the training cohort, and 151 patients from Shanghai Ninth People's Hospital were selected as the verification cohort. Independent variables were screened using Cox regression analysis, and a nomogram was constructed using R software. The prediction accuracy of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve. Decision curve analysis was used to evaluate the clinical application value of the model. The nomogram was externally validated using a validation cohort. ResultIn total, 559 patients with severe/critical COVID-19 were included in this study, of whom 179 (32.02 %) died. Multivariate Cox regression analysis showed that age >80 years [hazard ratio (HR) = 1.539, 95 % confidence interval (CI): 1.027–2.306, P = 0.037], history of diabetes (HR = 1.741, 95 % CI: 1.253–2.420, P = 0.001), high APACHE II score (HR = 1.083, 95 % CI: 1.042–1.126, P < 0.001), sepsis (HR = 2.387, 95 % CI: 1.707–3.338, P < 0.001), high neutrophil-to-lymphocyte ratio (NLR) (HR = 1.010, 95 % CI: 1.003–1.017, P = 0.007), and high D-dimer level (HR = 1.005, 95 % CI: 1.001–1.009, P = 0.028) were independent risk factors for 14 d and 28 d survival rates, whereas COVID-19 vaccination (HR = 0.625, 95 % CI: 0.440–0.886, P = 0.008) was a protective factor affecting prognosis. ROC curve analysis showed that the area under the curve (AUC) of the 14 d and 28 d hospital survival rates in the training cohort was 0.765 (95 % CI: 0.641–0.923) and 0.814 (95 % CI: 0.702–0.938), respectively, and the AUC of the 14 d and 28 d hospital survival rates in the verification cohort was 0.898 (95 % CI: 0.765–0.962) and 0.875 (95 % CI: 0.741–0.945), respectively. The calibration curves of 14 d and 28 d hospital survival showed that the predicted probability of the model agreed well with the actual probability. Decision curve analysis (DCA) showed that the nomogram has high clinical application value. ConclusionIn-hospital survival rates of patients with COVID-19 were predicted using a nomogram, which will help clinicians in make appropriate clinical decisions.

Nomogram-based prognostic tool for stage IIIB/IV non-small cell lung cancer patients undergoing traditional Chinese medicine treatment

ObjectiveGiven the significant impact of long-term traditional Chinese medicine (TCM) treatment on the prognosis of patients with non-small cell lung cancer (NSCLC), we aimed to develop nomograms, with or without consideration of TCM treatment duration, to accurately predict the overall survival (OS) of patients with stage IIIB/IV NSCLC treated with TCM. MethodsNomograms were developed from a training cohort comprised of 292 patients diagnosed with NSCLC, using univariate and multivariate Cox regression analyses to screen for various prognostic factors with and without TCM treatment. The nomograms were evaluated using the concordance index (C-index), calibration curve, and decision curve analysis (DCA), after which they were validated, using the bootstrap self-sampling method for internal validation, and a validation cohort comprised of 175 patients for external validation. Bootstrap validation is a resampling technique that involves randomly selecting and replacing data from the original dataset to make statistical inferences, thereby circumventing the issue of sample reduction that can arise from cross-validation. ResultsWe identified seven significant prognostic factors for OS. For nomogram A (excluding TCM treatment time), the C-indexes (95 % confidence interval [CI]) were 0.674 (0.635–0.712) and 0.660 (0.596–0.724) for the training and validation cohorts, respectively. For nomogram B (including TCM treatment time), the C-indices (95 % CI) were 0.846 (0.822–0.870) and 0.783 (0.730–0.894), for the training and validation cohorts, respectively, indicating that nomogram B was superior to nomogram A. Both the calibration curves and DCA results exhibited favorable clinical concordance and usefulness. ConclusionThe nomogram B yielded precise prognostic predictions for patients with advanced NSCLC treated with TCM.

Construction and Validation of a MRI‑Based Radiomic Nomogram to Predict Overall Survival in Patients with Local Advanced Cervical Cancer: A Multicenter Study

BackgroundCervical cancer is the fourth most common cancer among women. Radiomics has emerged as a new approach providing valuable information for cancer management. The aim of this study was to construct a radiomics nomogram to accurately predict survival outcomes in patients with locally advanced cervical cancer. MethodsThis retrospective study enrolled a total of 582 locally advanced cervical cancer patients from three center (training cohort: n=228; internal validation cohort: n=98; external validation cohort: n=256). Radiomic features were extracted from pretreatment MRI images. Least absolute shrinkage and selection operator logistic regression (LASSO) was applied to select radiomic features and calculated the radiomic scores. Univariate and multivariate Cox proportional hazards regression analyses were used to identify the independent prognostic clinic-radiological factors for cervical cancer, which were incorporated into the nomogram. ResultsA total of 6 radiomic features were found to be associated with overall survival of locally advanced cervical cancer patients. The AUC of radiomic scores in the training cohort was 0.634-0.708 for the training cohort, 0.725-0.762 for internal validation cohort and 0.788-0.881 for the external validation cohort. Age, parametrial invasion, and radiomic score were the independent prognostic indicators for cervical cancer patients (Age: HR=1.041, 95% CI=1.012-1.071, p=0.006; Parametrial invasion: HR=4.755, 95% CI=1.493-15.144, p=0.008; HR=2.324, 95% CI=1.050-5.143, p=0.037). The nomogram model incorporating these factors showed favorable discrimination in predicting the overall survival rates of cervical cancer patients, with the AUC values of 0.809, 0.808, and 0.862 for 1-, 2-, and 3-year predictions. The decision curve analysis (DCA) indicated that the nomogram model achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities. ConclusionThe nomogram, incorporating clinicopathological factors and radiomic features derived from MRI images, showed satisfactory discrimination in predicting the overall survival rates of locally advanced cervical cancer patients.

Construction and validation of "WCH-nomogram" for predicting the prognosis after resection of colorectal liver metastases.

The prognostic predictive tool for patients with colorectal liver metastasis (CRLM) is limited and the criteria for administering preoperative neoadjuvant chemotherapy in CRLM patients remain controversial. This study enrolled 532 CRLM patients at West China Hospital (WCH) from January 2009 to December 2019. Prognostic factors were identified from the training cohort to construct a WCH-nomogram and evaluating accuracy in the validation cohort. Receiver operating characteristic (ROC) curve analysis was used to compare the prediction accuracy with other existing prediction tools. From the analysis of the training cohort, four independent prognostic risk factors, namely tumor marker score, KRAS mutation, primary lymph node metastasis, and tumor burden score were identified on which a WCH-nomogram was constructed. The C-index of the two cohorts were 0.674 (95% CI: 0.634-0.713) and 0.655 (95% CI: 0.586-0.723), respectively, which was better than the previously reported predication scores (CRS, m-CS and GAME score). ROC curves showed AUCs for predicting 1-, 3-, and 5-year overall survival (OS) of 0.758, 0.709, and 0.717 in the training cohort, and 0.860, 0.669, and 0.692 in the validation cohort, respectively. A cutoff value of 114.5 points was obtained for the WCH-nomogram total score based on the maximum Youden index of the ROC curve of 5-year OS. Risk stratification showed significantly better prognosis in the low-risk group, however, the high-risk group was more likely to benefit from neoadjuvant chemotherapy. The WCH-nomogram demonstrates superior prognostic stratification compared to prior scoring systems, effectively identifying CRLM patients who may benefit the most from neoadjuvant chemotherapy.

Development and external validation of a prediction model for digit replantation failure after traumatic amputations based on a prospective multicenter cohort.

Failure of digit replantation after traumatic amputation is difficult to predict. The authors aimed to develop a prognostic model to better identify factors that better predict replantation failure following traumatic digit amputation. In this multicenter prospective cohort, the authors identified patients who had received digit replantation between 1 January 2015 and 1 January 2019. Univariable and multivariable analyses were performed successively to identify independently predictive factors for failure of replanted digit. To reduce overfitting, the Bayesian information criterion was used to reduce variables in the original model. Nomograms were created with the reduced model after model selection. This model was then internally validated with bootstrap resampling and further externally validated in validation cohort. Digit replantation was failed in 101 of 1062 (9.5%) digits and 146 of 1156 digits (12.6%) in the training and validation cohorts, respectively. The authors found that six independent prognostic variables were associated with digit replantation failure: age, mechanism of injury, ischemia duration, smoking status, amputation pattern (complete or incomplete), and surgeon's experience. The prediction model achieved good discrimination, with concordance indexes of 0.81 (95% CI: 0.76-0.85) and 0.70 (95% CI: 0.65-0.74) in predicting digit failure in the training and validation cohorts, respectively. Calibration curves were well-fitted for both training and validation cohorts. The proposed prediction model effectively predicted the failure rate of digit replantation for individual digits of all patients. It could assist in selecting the most suitable surgical plan for the patient.

Development and validation of a novel nomogram for predicting good neoangiogenesis after encephaloduroarteriosynangiosis in patients with moyamoya disease and type 2 diabetes mellitus: a case-control study.

Diabetes is often linked to poorer outcomes in patients with moyamoya disease (MMD). However, experience has shown that certain individuals with diabetes have favorable outcomes after encephaloduroarteriosynangiosis (EDAS). The authors aimed to develop a nomogram to predict good neoangiogenesis in patients with MMD and type 2 diabetes mellitus (T2DM) to aid neurosurgeons in the identification of suitable candidates for EDAS. Adults with MMD and T2DM who underwent EDAS between June 2004 and December 2018 were included in the analysis. In total, 126 patients (213 hemispheres) with MMD and T2DM from the Fifth Medical Centre of the Chinese PLA General Hospital were included and randomly divided into training (152 hemispheres) and internal validation (61 hemispheres) cohorts at a ratio of 7:3. Univariate logistic and least absolute shrinkage and selection operator regression analyses were used to identify the significant factors associated with good neoangiogenesis, which were used to develop a nomogram. The discrimination, calibration, and clinical utility were assessed. A total of 213 hemispheres in 126 patients were reviewed, including 152 (71.36%) hemispheres with good postoperative collateral formation and 61 (28.64%) with poor postoperative collateral formation. The authors selected 4 predictors (FGD5 rs11128722, VEGFA rs9472135, Suzuki stage, and internal carotid artery [ICA] moyamoya vessels) for nomogram development. The C-indices of the nomogram in the training and internal validation cohorts were 0.873 and 0.841, respectively. The nomogram exhibited a sensitivity of 84.5% and specificity of 81.0%. The positive and negative predictive values were 92.1% and 66.7%, respectively. The calibration curves indicated high predictive accuracy, and receiver operating characteristic curve analysis showed the superiority of the nomogram. The decision-making analysis validated the fitness and clinical application value of this nomogram. Then a web-based calculator to facilitate clinical application was generated. The nomogram developed in this study accurately predicted neoangiogenesis in patients with MMD and T2DM after EDAS and may assist neurosurgeons in identifying suitable candidates for indirect revascularization surgery.