Nomogram-aided individual induction chemotherapy regimen selection in advanced nasopharyngeal carcinoma

Pretreatment dual-energy CT for predicting early response to induction chemotherapy and survival in nasopharyngeal carcinoma

OBJECTIVES

To evaluate the predictive performance of pretreatment dual-energy CT (DECT) for early response to induction chemotherapy and survival in nasopharyngeal carcinoma (NPC).

METHODS

In this retrospective study, 56 NPC patients who underwent pretreatment DECT scans with posttreatment follow-up were enrolled. The DECT-derived normalised iodine concentration (nIC), effective atomic number (Zeff), 40-180 keV (20 keV interval), and Mix-0.3 value of the tumour lesions were measured to predict the early response to induction chemotherapy and survival in nasopharyngeal carcinoma. The Mann‒Whitney U test, ROC analysis, Kaplan‒Meier method with log-rank test, and Cox proportional hazards model were performed to evaluate the predictive performance of DECT parameters, respectively.

RESULTS

Among all DECT-derived parameters, ROC analysis showed the predictive performances of nIC and Zeff values for early objective response to induction chemotherapy (AUCs of 0.803 and 0.826), locoregional failure-free survival (AUCs of 0.786 and 0.767), progression-free survival (AUCs of 0.856 and 0.731) and overall survival (AUCs of 0.765 and 0.799) in NPC patients, respectively (all p < 0.05). Moreover, multivariate analysis showed that a high nIC value was an independent predictor of poor survival in NPC. In addition, survival analysis indicated that NPC patients with higher nIC values in primary tumours tend to have lower 5-year locoregional failure-free survival, progression-free survival and overall survival rates than those with lower nIC values.

CONCLUSIONS

DECT-derived nIC and Zeff values can predict early response to induction chemotherapy and survival in NPC; in particular, a high nIC value is an independent predictive factor of poor survival in NPC.

CLINICAL RELEVANCE STATEMENT

Preoperative dual-energy computed tomography may provide predictive value for early response and survival outcomes in patients with nasopharyngeal carcinoma, and facilitate their clinical management.

KEY POINTS

• Pretreatment dual-energy computed tomography helps to predict early response to therapy and survival in NPC. • NIC and Zeff values derived from dual-energy computed tomography can predict early objective response to induction chemotherapy and survival in NPC. • A high nIC value is an independent predictive factor of poor survival in NPC.

Predicting Bone Metastasis Risk Based on Skull Base Invasion in Locally Advanced Nasopharyngeal Carcinoma

Objective

To develop and validate a bone metastasis prediction model based on skull base invasion (SBI) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC).

Methods

This retrospective cohort study enrolled 290 patients with LA-NPC who received intensity-modulated radiation therapy in two hospitals from 2010 to 2020. Patient characteristics were grouped by SBI and hospital. Both unadjusted and multivariate-adjusted models were used to determine bone metastasis risk based on SBI status. Subgroup analysis was performed to investigate heterogeneity using a forest graph. Cox proportional hazard regression analysis was used to screen for risk factors of bone metastasis-free survival (BMFS). A nomogram of BMFS based on SBI was developed and validated using C-index, receiver operating characteristic curve, calibration curves, and decision curve analysis after Cox proportional hazard regression analysis.

Results

The incidence of bone metastasis was 14.83% (43/290), 20.69% (24/116), and 10.92% (19/174) in the overall population, SBI-positive group, and SBI-negative group, respectively. In the unadjusted model, SBI was associated with reduced BMFS [HR 2.43 (1.32–4.47), P = 0.004], and the results remained stable after three continuous adjustments (P <0.05). No significant interaction was found in the subgroup analyses (P for interaction >0.05). According to Cox proportional hazard regression analysis and clinical value results, potential risk factors included SBI, Karnofsky performance status, TNM stage, induction chemotherapy, concurrent chemoradiotherapy, and adjuvant chemotherapy. Using a training C-index of 0.80 and a validation C-index of 0.79, the nomogram predicted BMFS and demonstrated satisfactory prognostic capability in 2, 3, and 5 years (area under curve: 83.7% vs. 79.6%, 81.7% vs. 88.2%, and 79.0% vs. 93.8%, respectively).

Conclusion

Skull base invasion is a risk factor for bone metastasis in patients with LA-NPC. The SBI-based nomogram model can be used to predict bone metastasis and may assist in identifying LA-NPC patients at the highest risk of bone metastasis.