Efficacy of intensity-modulated radiotherapy with concurrent carboplatin in nasopharyngeal carcinoma

Background. The aim of the prospective phase II study was to evaluate the efficacy and toxicities of concurrent carboplatin with intensity-modulated radiotherapy (IMRT) in the treatment of nasopharyngeal carcinoma (NPC)

Prognostic value of plasma EBV DNA for nasopharyngeal cancer patients during treatment with intensity-modulated radiation therapy and concurrent chemotherapy

Plasma EBV DNA concentrations at the time of diagnosis (pre-EBV) and post treatment (post-EBV) have significant value for predicting the clinical outcome of nasopharyngeal cancer (NPC) patients. However, the prognostic value of the EBV concentration during radiation therapy (mid-EBV) has not been vigorously studied

A prospective randomized study comparing the efficacy between povidone-iodine gargling and benzydamine hydrochloride for mucositis prevention in head and neck cancer patients receiving concurrent chemoradiotherapy

Background: Concurrent chemoradiation (CCRT) has been the standard treatment for organ preservation or locally advanced head and neck cancer (LAHNC). Radiation-induced oral mucositis (RIOM) is an important treatment-limiting toxicity. Benzydamine hydrochloride was recommended to prevent oral mucositis. Povidone-iodine had also been adopted to use as an oral rinse to prevent mucositis. Objective: This study compared the efficacy between benzydamine hydrochloride and 0.1% povidone-iodine to prevent RIOM in HNC patients who received concurrent chemoradiotherapy. Methods: We conducted a randomized control study in HNC patients receiving CCRT with curative intent. The stratification factors were primary site of disease, treatment modality, chemotherapy regimen, and schedule. The primary outcome was RIOM assessed by Oral Mucositis Assessment Scale (OMAS). Secondary outcomes included RIOM assessed by NCI-CTCAE, use of analgesic, antibiotics and anti-fungal drugs, hospitalization, and participant satisfaction. Results: There were 83 participants recruited for this study with 71 completing the trial. Demographic characteristics were well-balanced between both arms. The univariate regression analysis revealed that povidone-iodine correlated with less RIOM compared to benzydamine hydrochloride (coefficient −2.25, 95% CI -4.37 to −0.012, p-value 0.03). The incidence of grade III-IV CTCAE RIOM during the study period was 51.4% with benzydamine hydrochloride compared to 26.5% with 0.1% povidone iodine (p-value 0.032). The peak incidence of grade III-IV CTCAE RIOM occurred in the 7th week of treatment (40.5% vs. 11.8%, p-value 0.01). This indicated the efficacy of povidone-iodine to prevent severe RIOM which usually most severity in the last week of CCRT treatment. The multivariate analysis revealed that the CCRT setting (definitive vs. adjuvant) and gargling agents (povidone-iodine vs. benzydamine hydrochloride were the factors associated with RIOM. Conclusion: This study demonstrated higher efficacy of 0.1% povidone-iodine gargle than benzydamine hydrochloride in mucositis prevention

Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma.

BackgroundThe prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment.PurposeTo investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC.Materials and methodsA retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme.ResultsIntegrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (pConclusionsThe combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone

Improved prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma using pre-treatment CT radiomics

Abstract When planning radiation therapy, late effects due to the treatment should be considered. One of the most common complications of head and neck radiation therapy is hypothyroidism. Although clinical and dosimetric data are routinely used to assess the risk of hypothyroidism after radiation, the outcome is still unsatisfactory. Medical imaging can provide additional information that improves the prediction of hypothyroidism. In this study, pre-treatment computed tomography (CT) radiomics features of the thyroid gland were combined with clinical and dosimetric data from 220 participants to predict the occurrence of hypothyroidism within 2 years after radiation therapy. The findings demonstrated that the addition of CT radiomics consistently and significantly improves upon conventional model, achieving the highest area under the receiver operating characteristic curve (AUCs) of 0.81 ± 0.06 with a random forest model. Hence, pre-treatment thyroid CT imaging provides useful information that have the potential to improve the ability to predict hypothyroidism after nasopharyngeal radiation therapy

Sparing Bilateral Neck Level IB in Oropharyngeal Carcinoma and Xerostomia Outcomes

ObjectivesTo assess whether sparing neck-level IB in target delineation of node-positive (N+) oropharyngeal carcinoma (OPC) can improve xerostomia outcomes without compromising locoregional control (LRC).MethodsA total of 125 N+ OPC patients with a median age of 57 years underwent chemoradiation between May 2010 and December 2011. A total of 74% of patients had T1-T2 disease, 26% T3-T4, 16% N1, 8% N2A, 48% N2B, 28% N2C; 53% base of tongue, 41% tonsil, and 6% other. Patients were divided into those who had target delineation sparing of bilateral level IB (the spared cohort) versus no sparing (the treated cohort). Sparing of contralateral high-level II nodes was also performed more consistently in the spared cohort. A prospective xerostomia questionnaire (patient reported) was given at each patient follow-up visit to this cohort of patients to assess late xerostomia. Clinical assessment (observer rated) at each patient follow-up visit was also recorded.ResultsThe 2-year LRC for the spared and treated cohorts was 97.5% and 93.8%, respectively (median follow-up, 23.2 mo). No locoregional failures occurred outside of treatment fields. The spared cohort experienced significant benefits in patient-reported xerostomia summary scores (P=0.021) and observer-rated xerostomia scores (P=0.006). In addition, there were significant reductions in mean doses to the ipsilateral submandibular gland (63.9 vs. 70.5 Gy; P<0.001), contralateral submandibular gland (45.0 vs. 56.2 Gy; P<0.001), oral cavity (35.9 vs. 45.2 Gy; P<0.001), and contralateral parotid gland (20.0 vs. 24.4 Gy; P<0.001).ConclusionsTarget delineation sparing of bilateral level IB nodes in N+ OPC reduced mean doses to salivary organs without compromising LRC. Patients with reduced target volumes had better patient-reported xerostomia outcomes

A predictive model of radiation-related fibrosis based on the radiomic features of magnetic resonance imaging and computed tomography.

Background: To establish a predictive model for the fibrotic level of neck muscles after radiotherapy by using radiomic features extracted from the magnetic resonance imaging (MRI) before and after radiotherapy and planning computed tomography (CT) in nasopharyngeal carcinoma patients. Methods: A total of one hundred and eighty-six patients were finally enrolled in this study. According to the specific standard, all patients were divided into three different fibrosis groups. Regions of interests (ROI), including sternocleidomastoids (SCMs), trapezius (T), levator scapulae (LS), and scalenus muscles (S), were delineated manually and used for features extraction on IBEX. XGBoost, a machine learning algorithm, was used for the establishment of the prediction model. First, the patients were divided into training cohort (80%) and testing cohort (20%) randomly. Then the image features of CT or delta changes calculated from pre- and post-radiotherapy MRI images on each cohort constituted training and testing datasets. Then, based on the training dataset, a well-trained prediction model was produced. We used five-fold cross-validation to validate the predictive models. Afterward, the model performance was assessed on the \u27testing\u27 set and reported in terms of area under the receiver operating characteristic curve (AUC) under five scenarios: (I) only T1 sequence, (II) only T2 sequence, (III) only T1 post-contrast (T1 + C) sequence, (IV) Combination of all MRI sequences, (V) only CT. Results: Most of the patients enrolled are male (73.1%), mean age was 47 years, receiving concurrent chemo-radiotherapy as the primary treatment (90.9%). By the end of the final follow-up, most of the patients were rated as mild fibrosis (60.8%). We found the prediction model based on the CT image features outperform all MRI features with an AUC of 0.69 and accuracy of 0.65. Contrarily, the model based on features from all MRI sequence showed lower AUC less than 0.5 and lower accuracy less than 0.6. Conclusions: The prediction model based on CT radiomics features has better performance in the prediction of the grade of post-radiotherapy neck fibrosis. This might help guide radiotherapy treatment planning to achieve a better quality of life

Clinical features and their coefficients in the clinical model for prediction.

(A) overall survival (OS), (B) progression-free survival (PFS), and (C) distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma. (PDF)</p

Patient demographics.

BackgroundThe prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment.PurposeTo investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC.Materials and methodsA retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme.ResultsIntegrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (ppConclusionsThe combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone.</div

Clinical and CT-based radiomic features and their coefficients in the combined model without Epstein-Barr Virus (EBV) value for prediction.

(A) overall survival (OS), (B) progression-free survival (PFS), and (C) distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma. (PDF)</p