UNASSIGNED: To assess the performance of the European Thyroid Association Thyroid Imaging and Reporting Data System (EU-TIRADS) and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS), which combine risk stratification systems for thyroid nodules (TN-RSS) and cervical lymph nodes (LN-RSS) in diagnosing malignant and metastatic thyroid cancer in a single referral center.
UNASSIGNED: We retrospectively analyzed 2,055 consecutive patients who underwent thyroidectomy or fine-needle aspiration (FNA) from January 2021 to December 2022. TNs and LNs were categorized according to the ultrasonography (US) features of EU-TIRADS and K-TIRADS, respectively. The diagnostic performance and postponed malignancy rate (PMR) were compared with those of EU-TIRADS and K-TIRADS. PMR was defined as the number of patients with malignant nodules not recommended for biopsy among patients with cervical LN metastasis.
UNASSIGNED: According to the EU-TIRADS and K-TIRADS, for TN-RSS alone, there were no significant differences in sensitivity, specificity, accuracy, unnecessary FNA rate (UFR), missed malignancy rate (MMR), and PMR between the two TIRADSs (29.0% vs. 28.8%, 50.5% vs. 51.1%, 32.3% vs. 32.2%, 23.6% vs. 23.5%, 88.6% vs. 88.5%, and 54.2% vs. 54.5%, P > 0.05 for all). Combining the LN-RSS increased the diagnostic accuracy (42.7% vs. 32.3% in EU-TIRADS; 38.8% vs. 32.2% in K-TIRADS) and decreased the PMR (54.2% vs. 33.9% in EU-TIRADS; 54.5% vs. 39.3% in K-TIRADS). EU-TIRADS had higher sensitivity and accuracy and lower PMR than K-TIRADS (41.3% vs. 36.7%, 42.7% vs. 38.8%,33.9% vs. 39.3%, P < 0.05 for all).
UNASSIGNED: A combination of TN-RSS and LN-RSS for the management of thyroid nodules may be associated with a reduction in PMR, with enhanced sensitivity and accuracy for thyroid cancers in EU-TIRADS and K-TIRADS. These results may offer a new direction for the detection of aggressive thyroid cancers.

UNASSIGNED: Thyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients.
UNASSIGNED: We collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model.
UNASSIGNED: A total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805-0.927), for the validation cohort it was 0.944 (95 % CI: 0.903-0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997-1.001). Calibration curves and AUC suggest that the model has good accuracy.
UNASSIGNED: We developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.

UNASSIGNED: Adrenocortical carcinoma (ACC) is an extremely rare and highly invasive malignant tumor. However, there is currently no reliable method to predict the prognosis of ACC. Our objective is to construct a nomogram and a risk classification system to predict the 1-year, 3-year, and 5-year overall survival (OS) of ACC.
UNASSIGNED: We retrieved clinicopathological data of patients diagnosed with ACC in The Surveillance, Epidemiology, and End Results (SEER) database and divided them into training and validation cohorts with a 7:3 ratio. Simultaneously, we collected an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). Univariate and multivariate Cox analyses were performed to identify relevant risk factors, which were then combined to develop a correlation nomogram. The predictive performance of the nomogram was evaluated using the concordance index (C-index), receiver-operating characteristic curve (ROC), and calibration curves. Decision curve analysis (DCA) was applied to assess the clinical utility of the nomogram. In addition, Kaplan-Meier survival curves were generated to demonstrate the variation in OS between groups.
UNASSIGNED: The final nomogram consisted of five factors: age, T, N, M, and history of chemotherapy. Our prognostic model demonstrated significant discriminative ability, with C-index and the area under the receiver operating characteristic (AUC) values exceeding 0.70. Additionally, DCA validated the clinical utility of the nomogram. In the entire cohort, the median OS for patients in the low- and high-risk groups was 70 and 10 months, respectively.
UNASSIGNED: A nomogram and a corresponding risk classification system were developed in order to predict the OS of patients diagnosed with ACC. These tools have the potential to provide valuable support for patient counseling and assist in the decision-making process related to treatment options.

BACKGROUND: The study determined to construct a novel predictive nomogram to access the prognosis of pancreatic cancer patients with liver metastases (PCLM).
METHODS: Medical records included clinical and laboratory variables were collected. The patients were randomly divided into training and validation cohort. First, in the training cohort, the optimal cutoff value of SII, PNI, NLR, PLR were obtained. Then the survival analysis evaluated the effects of above indices on OS. Next, univariate and multivariate analyses were used to identify the independent factors of OS. Moreover, a nomogram was constructed based on LASSO cox analysis. Additionally, the predictive efficacy of the nomogram was evaluated by ROC curve and calibration curve in the training and validation cohort. Finally, a risk stratification system based on the nomogram was performed.
RESULTS: A total of 472 PCLM patients were enrolled in the study. The optimal cutoff values of SII, PNI, PLR and NLR were 372, 43.6, 285.7143 and 1.48, respectively. By combing SII and PNI, named coSII-PNI, we divided the patients into three groups. The Kaplan-Meier curves demonstrated above indices were correlated with OS. Univariate and multivariate analyses found the independent prognostic factors of OS. Through LASSO cox analysis, coSII-PNI, PNI, NLR, CA199, CEA, chemotherapy and gender were used to construct the nomogram. Lastly, the ROC curve and calibration curve demonstrated that the nomogram can predict prognosis of PCLM patients. Significant differences were observed between high and low groups.
CONCLUSIONS: The nomogram based on immune, inflammation, nutritional status and other clinical factors can accurately predict OS of PCLM patients.

OBJECTIVE: Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFT-P) is an encapsulated follicular variant of papillary thyroid carcinoma (PTC) with nonaggressive clinical behavior. However, since its diagnosis is exclusively possible after surgery, it represents a clinical challenge. Neck ultrasound (US) shows good sensitivity and specificity in suggesting malignancy in thyroid nodules. However, little information is available about its ability in identifying NIFT-P.
METHODS: The aim of this study was to evaluate the US features of NIFT-P, comparing them with other follicular-patterned thyroid tumors, and to test the ability of the main US risk stratification system (RSS) in identifying NIFT-P.
METHODS: We retrospectively evaluated 403 consecutive patients submitted to thyroid surgery, with positive histology for at least 1 nodule being NIFT-P, follicular variant of PTC (FV-PTC), follicular thyroid carcinoma (FTC), or follicular adenoma (FA).
RESULTS: The US features of NIFT-P (n = 116), FV-PTC (n = 170), FTC (n = 76), and FA (n = 90) were reported. Follicular variant of PTC and FTC more frequently showed irregular margins, presence of calcifications, \"taller than wide\" shape, and the absence of halo compared with NIFT-P. Furthermore, FTC and also FA were larger and more frequently hypoechoic than NIFT-P. Most cases (77%) showed an indeterminate cytology. Regardless of the US RSS considered, NIFT-P and FA were less frequently classified in the high-suspicious category compared with FV-PTC and FTC.
CONCLUSIONS: Ultrasound features of NIFT-P are frequently superimposable to those of nodules with low suspicion of malignancy. The NIFT-P is almost never classified in the high-suspicious category according to the main US RSS. Therefore, although the preoperative identification of NIFT-P remains a challenge, neck US can be integrated in the algorithm of management of nodules with indeterminate cytology, suggesting a possible conservative approach in those with low-suspicious features.

METHODS: Retrospective cohort study.
OBJECTIVE: Spinal cord astrocytoma (SCA) is a rare central nervous system malignancy that typically requires early surgical intervention. However, the substantial frequency of relapse and bad outcomes limit the surgical advantage for patients. Herein, we aimed to determine the independent prognostic factors of cancer-specific survival (CSS) in post-surgical patients with primary SCA and to develop a new method to estimate the chances of CSS in these patients at 3-, 5- and 10-year.
METHODS: A total of 364 postoperative patients with SCA were recruited from the Surveillance, Epidemiology, and End Results database and randomly assigned to the training and validation sets. Univariate and multivariate Cox regression assessments were used to identify independent prognostic indicators. Second, a nomogram was established by integrating these indicators to estimate 3-, 5-, and 10-year CSS in patients with SCA who underwent surgery. Subsequently, the discriminatory power and predictive performance of the nomogram were assessed using the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). Finally, a mortality risk stratification system was generated.
RESULTS: Age, tumor stage, histological type, and radiotherapy were recognized as potential predictive indicators of CSS for postoperative patients with SCA. The ROC curve and DCA indicate that the nomogram has good accuracy and high clinical utility. Furthermore, the mortality risk stratification system efficiently divides patients into 3 risk subgroups.
CONCLUSIONS: The nomogram could accurately anticipate the 3-, 5-, and 10-year percentages of CSS in postoperative patients with SCA. It could assist clinicians with personalized medical counseling, risk stratification management, and clinical decision-making, improving the clinical outcomes of these patients.

BACKGROUND: Neuroblastoma (NB) is a childhood malignancy with marked heterogeneity, resulting in highly variable outcomes among patients. This study aims to establish a novel nomogram and risk stratification system to predict the overall survival (OS) for patients with NB.
METHODS: We analyzed neuroblastoma patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The nomogram was constructed using independent risk factors for OS, identified through univariate and multivariate Cox regression analyses. The accuracy of this nomogram was evaluated with the concordance index, receiver operating characteristic curve, calibration curve, and decision curve analysis. In addition, we developed a risk stratification system based on the total score of each patient in the nomogram.
RESULTS: A total of 2185 patients were randomly assigned to the training group and the testing group. Six risk factors, including age, chemotherapy, brain metastases, primary site, tumor stage, and tumor size, were identified in the training group. Using these factors, a nomogram was constructed to predict 1-, 3-, and 5-year OS of NB patients. This model exhibited superior accuracy in the training and testing groups, exceeding traditional tumor stage prediction. Subgroup analysis suggested worse prognosis for retroperitoneal origin in the intermediate-risk group and adrenal gland origin in the high-risk group compared to other sites. Additionally, the prognosis for high-risk patients significantly improved after surgery. We also developed a web application to make the nomogram more user-friendly in clinical practices.
CONCLUSIONS: This nomogram demonstrates excellent accuracy and reliability, offering more precise personalized prognostic predictions to clinical patients.

BACKGROUND: Little research has been done on the factors affecting the survival of patients with non-cirrhotic hepatocellular carcinoma (HCC-NCL). Our aim was to develop and validate a nomogram and a new risk stratification system that can evaluate overall survival (OS) in HCC-NCL patients.
METHODS: We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 to study HCC-NCL patients. The patients were randomly split into training and validation groups at a 7:3 ratio and subjected to single-factor and multi-factor COX regression analysis. We then developed a nomogram and evaluated its accuracy and clinical validity using time-dependent ROC, DCA, and calibration curves. We compared the nomogram with the AJCC staging system by calculating C-index, NRI, and IDI. Finally, we used Kaplan-Meier curves to compare the nomogram and AJCC staging. These analyses were performed without altering the original intended meaning.
RESULTS: AFP levels, surgical intervention, T-stage, tumor size, and M-stage were independent prognostic indicators for overall survival among the HCC-NCL population studied. We developed a nomogram based on these factors, and time-dependent ROC, calibration curves, DCA analyses, and C-index proved its accuracy. Compared to the AJCC staging system, the nomogram showed better prognostic accuracy through time-dependent ROC, DCA analyses, C-index, NRI, IDI, and Kaplan-Meier curves.
CONCLUSIONS: We have developed and validated a survival nomogram applicable to HCC-NCL patients, with risk stratification. Our nomogram offers personalized treatment and management options superior to those provided by the AJCC staging system.

BACKGROUND: Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery.
METHODS: Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC.
RESULTS: A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy.
CONCLUSIONS: Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.

BACKGROUND: The aim of this study was to construct two web-based nomograms to predict the probability of bone metastasis (BM) in esophageal cancer (EC) patients and the prognostic of EC patients with BM (ECBM).
METHODS: We collected the data of EC and ECBM patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015. Independent risk variables for the development of BM in EC patients were identified using univariate and multivariate logistic regression analyses. Univariate and multivariate Cox regression analyses were used to assess independent prognostic variables in ECBM patients. And then, constructed two nomograms to predict the risk of bone metastases and overall survival (OS) of ECBM patients. Survival differences were studied by Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical applicability of these two nomograms were assessed by using receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve and decision curve analysis (DCA).
RESULTS: We selected a total of 6839 patients with EC, of which 326 (4.77%) had BM at the time of initial diagnosis. The results of K-M survival and Cox regression analysis showed significant effects of BM on the OS in EC patients. Age, N stage, tumor size and brain/liver/lung organ metastasis were identified as BM-related risk variables. Chemotherapy and brain/liver organ metastasis were identified as ECBM-related prognostic variables. The ROC, AUC, calibration curves and DCA of two nomograms all showed excellent predictive efficacy and clinical applicability.
CONCLUSIONS: These two nomograms were constructed and validated, which could objectively predict the risk of BM in EC patients and the prognostic in ECBM patients. These tools are expected to make valuable contributions in clinical work, informing surgeons in making decisions about patient care.