A novel dosimetric metrics-based risk model to predict local recurrence in nasopharyngeal carcinoma patients treated with intensity-modulated radiation therapy

Enhancement of the effect of novel targeted 5-aminolevulinic acid conjugated bismuth oxide nanoparticles-based photodynamic therapy by simultaneous radiotherapy on KB cells

BACKGROUND

Selective accumulation of photosensitizers into cancerous cells is one of the most important factors affecting photodynamic therapy (PDT) efficacy. 5-aminolevulinic acid (5-ALA) is the precursor of a strong photosensitizer, protoporphyrin-IX; but it has poor permeability into the cells. Folate receptors are overexpressed on the surface of many tumor cells. In the present study, folic acid (FA) and 5-ALA conjugated bismuth oxide nanoparticles were synthesized; and used in PDT, radiotherapy (RT), and concurrent PDT & RT against nasopharyngeal carcinoma (KB cell line).

METHODS

The KB cells were incubated with the synthesized nanoparticles (NPs) for 2 h; then illuminated using a custom-made LED lamp at the light dose of 26 J/cm2. Irradiation of the cells was carried out using X-ray 6 MV (2 Gy); and synergistic effect of the simultaneous RT and PDT treatments was evaluated using fractional product values. Efficacy of the treatments was determined using MTT and Caspase-3 enzyme activity assays.

RESULTS

Targeting of folic acid receptors enables the selective endocytosis of the conjugated NPs. RT results in the presence of Bi2O3 NPs showed a significant radiosensitizer potential of these NPs. Fractional product values of 1.49±0.05, 1.36±0.06, and 1.05±0.06 obtained in the presence of FA-5-ALA conjugated NPs, 5-ALA conjugated NPs, and in the absence of the NPs, respectively. Therefore, simultaneous RT and PDT in the presence of these conjugated NPs is superior to RT in the presence of the NPs.

CONCLUSION

Simultaneous PDT and RT in the presence of FA-5-ALA conjugated bismuth oxide NPs can be introduced as a promising therapeutic approach in controlling KB cancer cells.

Nomograms containing body dose parameters for predicting survival in patients with nasopharyngeal carcinoma

PURPOSE

To assess the impact of body dose on survival outcomes in nasopharyngeal carcinoma (NPC) patients and to create novel nomograms incorporating body dose parameters for predicting survival.

METHODS

594 of non-metastasis NPC patients (training group, 396; validation group, 198) received intensity-modulated radiation therapy at our institution from January 2012 to December 2016. Patient characteristics, body dose parameters in dose-volume histogram (DVH) and hematology profiles were collected for predicting overall survival (OS) and progression-free survival (PFS). Nomograms for OS and PFS were developed using the selected predictors. Each nomogram was evaluated based on its C-index and calibration curve.

RESULTS

Body dose-based risk score for OS (RSOS), N stage, age, and induction chemotherapy were independent predictors for OS, with a C-index of 0.784 (95% CI 0.749-0.819) in the training group and 0.763 (95% CI 0.715-0.810) in the validation group for the nomogram. As for PFS, the most important predictors were the body dose-based risk score for PFS (RSPFS), N stage, and induction chemotherapy. C-index of PFS nomogram was 0.706 (95% CI 0.681-0.720) in the training group and 0.691 (95% CI 0.662-0.711) in the validation group. The two models outperformed the TNM staging system in predicting outcomes.

CONCLUSIONS

Body dose coverage is a useful predictor of prognosis in clinical routine patients. The novel nomograms integrating body dose parameters can precisely predict OS and PFS in NPC patients.

Radiomics-clinical nomogram based on pretreatment 18F-FDG PET-CT radiomics features for individualized prediction of local failure in nasopharyngeal carcinoma

To explore the prognostic significance of PET/CT-based radiomics signatures and clinical features for local recurrence-free survival (LRFS) in nasopharyngeal carcinoma (NPC). We retrospectively reviewed 726 patients who underwent pretreatment PET/CT at our center. Least absolute shrinkage and selection operator (LASSO) regression and the Cox proportional hazards model were applied to construct Rad-score, which represented the radiomics features of PET-CT images. Univariate and multivariate analyses were used to establish a nomogram model. The concordance index (C-index) and calibration curve were used to evaluate the predictive accuracy and discriminative ability. Receiver operating characteristic analysis was performed to stratify the local recurrence risk of patients. The nomogram was validated by evaluating its discrimination ability and calibration in the validation cohort. A total of eight features were selected to construct Rad-score. A radiomics-clinical nomogram was built after the selection of univariate and multivariable Cox regression analyses, including the Rad-score and maximum standardized uptake value (SUVmax). The C-index was 0.71 (0.67-0.74) in the training cohort and 0.70 (0.64-0.76) in the validation cohort. The nomogram also performed far better than the 8th T-staging system with an area under the receiver operating characteristic curve (AUC) of 0.75 vs. 0.60 for 2 years and 0.71 vs. 0.60 for 3 years. The calibration curves show that the nomogram indicated accurate predictions. Decision curve analysis (DCA) revealed significantly better net benefits with this nomogram model. The log-rank test results revealed a distinct difference in prognosis between the two risk groups. The PET/CT-based radiomics nomogram showed good performance in predicting LRFS and showed potential to identify patients at high-risk of developing NPC.

Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer-Specific Survival Using Electronic Health Record Data: A Multisite Study

PURPOSE

There is limited knowledge of the prediction of 2-year cancer-specific survival (CSS) in the head and neck cancer (HNC) population. The aim of this study is to develop and validate machine learning models and a nomogram for the prediction of 2-year CSS in patients with HNC using real-world data collected by major teaching and tertiary referral hospitals in New South Wales (NSW), Australia.

MATERIALS AND METHODS

Data collected in oncology information systems at multiple NSW Cancer Centres were extracted for 2,953 eligible adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Death data were sourced from the National Death Index using record linkage. Machine learning and Cox regression/nomogram models were developed and internally validated in Python and R, respectively.

RESULTS

Machine learning models demonstrated highest performance (C-index) in the larynx and nasopharynx cohorts (0.82), followed by the oropharynx (0.79) and the hypopharynx and oral cavity cohorts (0.73). In the whole HNC population, C-indexes of 0.79 and 0.70 and Brier scores of 0.10 and 0.27 were reported for the machine learning and nomogram model, respectively. Cox regression analysis identified age, T and N classification, and time-corrected biologic equivalent dose in two gray fractions as independent prognostic factors for 2-year CSS. N classification was the most important feature used for prediction in the machine learning model followed by age.

CONCLUSION

Machine learning and nomogram analysis predicted 2-year CSS with high performance using routinely collected and complete clinical information extracted from oncology information systems. These models function as visual decision-making tools to guide radiotherapy treatment decisions and provide insight into the prediction of survival outcomes in patients with HNC.