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Objectives: We sought to identify the predictors of delayed viral clearance in patients with cancer with asymptomatic COVID-19 when the SARS-CoV-2 Omicron variants prevailed in Hong Kong., Methods: All patients with cancer who were attending radiation therapy for head and neck malignancies or systemic anticancer therapy saved their deep throat saliva or nasopharyngeal swabs at least twice weekly for SARS-CoV-2 screening between January 1 and April 30, 2022. The multivariate analyses identified predictors of delayed viral clearance (or slow recovery), defined as >21 days for the cycle threshold values rising to ≥30 or undetectable in two consecutive samples saved within 72 hours. Three machine learning algorithms evaluated the prediction performance of the predictors., Results: A total of 200 (15%) of 1309 patients tested positive for SARS-CoV-2. Age >65 years (P = 0.036), male sex (P = 0.003), high Charlson comorbidity index (P = 0.042), lung cancer (P = 0.018), immune checkpoint inhibitor (P = 0.036), and receipt of one or no dose of COVID-19 vaccine (P = 0.003) were significant predictors. The three machine learning algorithms revealed that the mean ± SD area-under-the-curve values predicting delayed viral clearance with the cut-off cycle threshold value ≥30 was 0.72 ± 0.11., Conclusion: We identified subgroups with delayed viral clearance that may benefit from targeted interventions., Competing Interests: Declaration of competing interest VHFL reported receiving personal fees and grants from AstraZeneca and personal fees from AQUILAB, Amgen, Boston Scientific, Eli Lilly, Merck Sharp and Dohme, Novartis, Pfizer, and Takeda outside the submitted work. All other authors have no competing interests to declare., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background and Purpose: To investigate the radiomic feature (RF) repeatability via perturbation and its impact on cross-institutional prognostic model generalizability in Nasopharyngeal Carcinoma (NPC) patients., Materials and Methods: 286 and 183 NPC patients from two institutions were included for model training and validation. Perturbations with random translations and rotations were applied to contrast-enhanced T1-weighted (CET1-w) MR images. RFs were extracted from primary tumor volume under a wide range of image filtering and discretization settings. RF repeatability was assessed by intraclass correlation coefficient (ICC), which was used to equally separate the RFs into low- and high-repeatable groups by the median value. After feature selection, multivariate Cox regression and Kaplan-Meier analysis were independently employed to develop and analyze prognostic models. Concordance index (C-index) and P-value from log-rank test were used to assess model performance., Results: Most textural RFs from high-pass wavelet-filtered images were susceptible to image perturbations. It was more prominent when a smaller discretization bin number was used (e.g., 8, mean ICC = 0.69). Using high-repeatable RFs for model development yielded a significantly higher C-index (0.63) in the validation cohort than when only low-repeatable RFs were used (0.57, P = 0.024), suggesting higher model generalizability. Besides, significant risk stratification in the validation cohort was observed only when high-repeatable RFs were used (P < 0.001)., Conclusion: Repeatability of RFs from high-pass wavelet-filtered CET1-w MR images of primary NPC tumor was poor, particularly when a smaller bin number was used. Exclusive use of high-repeatable RFs is suggested to safeguard model generalizability for wide-spreading clinical utilization., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

(1) Background: NPC patients with de novo distant metastasis appears to be a heterogeneous group who demonstrate a wide range of survival, as suggested by growing evidence. Nevertheless, the current 8th edition of TNM staging (TNM-8) grouping all these patients into the M1 category is not able to identify their survival differences. We sought to identify any anatomic and non-anatomic subgroups in this study. (2) Methods: Sixty-nine patients with treatment-naive de novo M1 NPC (training cohort) were prospectively recruited from 2007 to 2018. We performed univariable and multivariable analyses (UVA and MVA) to explore anatomic distant metastasis factors, which were significantly prognostic of overall survival (OS). Recursive partitioning analysis (RPA) with the incorporation of significant factors from MVA was then performed to derive a new set of RPA stage groups with OS segregation (Set 1 Anatomic-RPA stage groups); another run of MVA was performed with the addition of pre-treatment plasma EBV DNA. A second-round RPA with significant prognostic factors of OS identified in this round of MVA was performed again to derive another set of stage groups (Set 2 Prognostic-RPA stage groups). Both sets were then validated externally with an independent validation cohort of 67 patients with distant relapses of their initially non-metastatic NPC (rM1) after radical treatment. The performance of models in survival segregation was evaluated by the Akaike information criterion (AIC) and concordance index (C-index) under 1000 bootstrapping samples for the validation cohort; (3) Results: The 3-year OS and median follow-up in the training cohort were 36.0% and 17.8 months, respectively. Co-existence of liver-bone metastases was the only significant prognostic factor of OS in the first round UVA and MVA. Set 1 RPA based on anatomic factors that subdivide the M1 category into two groups: M1a (absence of co-existing liver-bone metastases; median OS 28.1 months) and M1b (co-existing liver-bone metastases; median OS 19.2 months, p = 0.023). When pre-treatment plasma EBV DNA was also added, it became the only significant prognostic factor in UVA (p = 0.001) and MVA (p = 0.015), while co-existing liver-bone metastases was only significant in UVA. Set 2 RPA with the incorporation of pre-treatment plasma EBV DNA yielded good segregation (M1a: EBV DNA ≤ 2500 copies/mL and M1b: EBV DNA > 2500 copies/mL; median OS 44.2 and 19.7 months, respectively, p < 0.001). Set 2 Prognostic-RPA groups (AIC: 228.1 [95% CI: 194.8−251.8] is superior to Set 1 Anatomic-RPA groups (AIC: 278.5 [254.6−301.2]) in the OS prediction (p < 0.001). Set 2 RPA groups (C-index 0.59 [95% CI: 0.54−0.67]) also performed better prediction agreement in the validation cohort (vs. Set 1: C-index 0.47 [95% CI: 0.41−0.53]) (p < 0.001); (4) Conclusions: Our Anatomic-RPA stage groups yielded good segregation for de novo M1 NPC, and prognostication was further improved by incorporating plasma EBV DNA. These new RPA stage groups for M1 NPC can be applied to countries/regions regardless of whether reliable and sensitive plasma EBV DNA assays are available or not.

Purpose: To propose a refined M1 classification in de novo metastatic nasopharyngeal carcinoma (NPC) based on pooled data from two academic institutions., Methods: Previously untreated de novo M1 NPC patients prospectively treated at The University of Hong Kong (N = 69) and Fujian Cancer Hospital (N = 114) between 2007 and 2016 were recruited and randomized in a 2:1 ratio to generate training (N = 120) and validation (N = 63) cohorts, respectively. Multivariable analysis (MVA) was performed for the training and validation cohorts to identify anatomic prognostic factors for overall survival (OS). Recursive partitioning analysis (RPA) was performed which incorporated the anatomic prognostic factors identified in the MVA to derive Anatomic-RPA groups which stratified OS in the training cohort, and were then validated in the validation cohort., Results: Median follow-up for the training and validation cohorts was 27.2 and 30.2 months with 3-year OS of 51.6% and 51.1%, respectively. MVA revealed that co-existing liver-bone metastases was the only factor prognostic for OS in both the training and validation cohorts. Anatomic-RPA separated M1 disease into M1a (no co-existing liver-bone metastases) and M1b (co-existing liver-bone metastases) with median OS 39.5 and 23.7 months, respectively (p = 0.004) in the training cohort. RPA for the validation cohort also confirmed good segregation with co-existing liver-bone metastases with median OS 47.7 and 16.0 months, respectively (p = 0.008)., Conclusion: Our proposal to subdivide de novo M1 NPC into M1a (no co-existing liver-bone metastases) vs. M1b (co-existing liver-bone metastases) provides better OS segregation., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for achieving efficient ART eligibility screening, while no relevant studies have been reported. Hence, we aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting IfTM-triggered ART events in NPC patients via a multi-center setting. Contrast-enhanced CT and the clinical data of 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic (R), clinical (C), and combined (RC) models were developed using the ridge algorithm in the QEH cohort and evaluated in the QMH cohort using the median area under the receiver operating characteristics curve (AUC). Delong's test was employed for model comparison. Model performance was further assessed on 1000 replicates in both cohorts separately via bootstrapping. The R model yielded the highest "corrected" AUC of 0.784 (BCa 95%CI: 0.673-0.859) and 0.723 (BCa 95%CI: 0.534-0.859) in the QEH and QMH cohort following bootstrapping, respectively. Delong's test indicated that the R model performed significantly better than the C model in the QMH cohort ( p < 0.0001), while demonstrating no significant difference compared to the RC model ( p = 0.5773). To conclude, CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients in this multi-center study, outperforming the traditional clinical model. The findings of this study provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the long run, ultimately alleviating the workload of clinical practitioners, streamlining ART procedural efficiency in clinics, and achieving personalized RT for NPC patients in the future.

Background: This study analyzed the dose volume effects of re-irradiation for locally recurrent nasopharyngeal carcinoma (NPC) and attempts to determine the optimal dose for the best survival., Methods: Ninety-one patients were studied. The local control, fatal complication, and overall survival were analyzed against the dose (in Equivalent Dose at 2 Gy/fractions) and recurrent gross tumor volume (GTV)., Results: The local control and fatal complication rate appear to increase with prescribed dose. The overall survival peaks at around 60 Gy 10 . Local control decreases significantly with increasing GTV (P < .001) while overall survival shows similar trend (P = .06). No correlation was observed between the fatal complication rate and GTV volume. The dose response of local control appears to be stronger for smaller tumors., Conclusion: GTV volume plays a significant role in local control. A 60 Gy 10 appears to be optimal for the best survival outcome; higher doses might be considered for small tumors., (© 2019 Wiley Periodicals, Inc.)

Background: This study evaluates the contemporary care for patients with locally recurrent nasopharyngeal carcinoma after failure of the primary course of intensity modulated radiotherapy., Methods: Eligible patients were identified through the Hong Kong Cancer Registry database. Patterns of care and treatment outcomes were analyzed., Results: Two hundred seventy-two patients with locally recurrent tumors were identified. Of them, 30.9% received surgery, whereas 35.7% received re-irradiation (re-RT). The 5-year overall survival (OS) for the whole group was 30.2%. Old age and advanced rT classification were adverse prognostic factors, whereas surgery (mainly in resectable recurrence) was associated with favorable survival outcome. The 5-year OS rates for patients who received surgery and re-RT were 56.3% and 21.8%, respectively., Conclusions: Early detection of resectable recurrence is of paramount importance as surgery for resectable tumors offers the potential to achieve excellent outcomes. Re-RT could be considered in selected patients with unresectable disease and favorable prognostic features., (© 2019 Wiley Periodicals, Inc.)