A nomogram to predict survival and guide individualized induction chemotherapy in T3-4N1M0 nasopharyngeal carcinoma

The Risk Assessment Before Dose Tapering Among Methadone Maintenance Treatment Participants: Derivation and Validation of a Nomogram

Many methadone maintenance treatment (MMT) participants experienced a tapering phase. The benefit of tapering is based on a balance between meeting the desire to reduce methadone dose and reduction in relapse. We aimed to develop and validate a nomogram to assess relapse risk after dose tapering. We developed and internally validated a nomogram for risk assessment before dose tapering in 432 participants with dose tapering in the non-Guangzhou region of Guangdong, China, and externally validated it in 117 participants with dose tapering in Guangzhou. Cox regression analysis showed that the taper start week (HR = 0.14, [0.08-0.22]) was an independent risk predictor of the relapse risk after tapering. The C-index of the nomogram was 0.76 (95%CI: 0.73-0.79) in the training cohort, 0.76 (95%CI: 0.72-0.80) in the testing cohort, and 0.84 (95%CI: 0.80-0.88) in the validation cohort. Decision curve analysis showed that the nomogram had better discriminative ability than other predictors. The nomogram was developed to assess the risk of relapse for MMT participants who volunteer a tapering phase and may help participants better make decisions about whether and how to reduce the dose to minimize the harm of relapse.

Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics

Purpose

The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC).

Methods

In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups.

Results

The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66.

Conclusion

The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.

Development and validation of nomogram models for predicting postoperative prognosis of early-stage laryngeal squamous cell carcinoma

BACKGROUND

We aimed to investigate the postoperative prognosis in patients with early-stage laryngeal squamous cell carcinoma (LSCC) in association with the preoperative blood markers and clinicopathological characteristics and to develop nomograms for individual risk prediction.

METHODS

The clinical data of 353 patients with confirmed early-stage LSCC between 2009 and 2018 were retrospectively retrieved from the First Affiliated Hospital with Nanjing Medical University. All patients were randomly divided into the training and testing groups in a 7:3 ratio. Univariate and multivariate analyses were performed, followed by the construction of nomograms to predict recurrence-free survival (RFS) and overall survival (OS). Finally, the nomograms were verified internally, and the predictive capability of the nomograms was evaluated and compared with that of tumour T staging.

RESULTS

Univariate and multivariate analyses identified platelet counts (PLT), fibrinogen (FIB), and platelet to lymphocyte ratio (PLR) were independent factors for RFS, and FIB, systemic immune-inflammation index (SII), and haemoglobin (HGB) were independent prognostic factors for OS. The nomograms showed higher predictive C-indexes than T staging. Furthermore, decision curve analysis (DCA) revealed that the net benefit of the nomograms' calculation model was superior to that of T staging.

CONCLUSIONS

We established and validated nomograms to predict postoperative 1-, 3- and 5-year RFS and OS in patients with early-stage LSCC based on significant blood markers and clinicopathological characteristics. These models might help clinicians make personalized treatment decisions.

Developing a nomogram model and prognostic analysis of nasopharyngeal squamous cell carcinoma patients: a population-based study

BACKGROUND

Nasopharyngeal squamous cell carcinoma (NPSCC) is a common histo-logical subtype of nasopharyngeal cancer with a generally poor prognosis. The aim of this study is to identify factors affecting the survival prognosis of NPSCC patients and develop a specialized nomogram model.

METHODS

We extracted clinical data of 1235 diagnosed cases of NPSCC from the SEER database using SEER*Stat software. Univariate and multivariate Cox proportional hazards regression analyses were conducted to explore clinical factors that impact the prognosis of NPSCC patients. Based on significant independent factors, we developed a nomogram to predict the 1, 3, and 5 years overall survival rates. The discriminative and predictive abilities of the nomogram were evaluated using C-index, calibration curve, area under the curve (AUC), and receiver operating characteristic curve. We evaluated the clinical value of the nomogram using decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

We performed a cohort analysis on 846 patients with nasopharyngeal cancer in the training cohort. Multivariate Cox regression analysis revealed age, race, marital status, primary tumor, radiation therapy, chemotherapy, SJCC stage, primary tumor size, Lung metastasis and brain metastasis as independent prognostic factors for NPSCC patients, which we used to construct the nomogram prediction model. The C-index of the training cohort was 0.737. The ROC curve analysis indicated that the AUC of the OS rate at 1, 3, and 5 years in the training cohort was > 0.75. The calibration curves of the two cohorts showed good consistency between the predicted and observed results. DCA and CIC demonstrated that the nomogram prediction model had good clinical benefits.

CONCLUSIONS

The nomogram risk prediction model for NPSCC patient survival prognosis, constructed in this study, has exhibited excellent predictive capability. This model can be employed for swift and precise assessment of individualized survival prognosis. It can offer valuable guidance to clinical physicians in diagnosing and treating NPSCC patients.