Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability.

Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test-retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test-retest model. Similar optimal reliability can be achieved with testing AUC = 0.7-0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test-retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.

Study protocol of ConquerFear-HK: a randomised controlled trial of a metacognition-based, manualised intervention for fear of cancer recurrence among Chinese cancer survivors.

INTRODUCTION: Fear of cancer recurrence (FCR) is a prevalent and frequently debilitating response to a cancer diagnosis, affecting a substantial proportion of cancer survivors. Approximately 30% of local Hong Kong Chinese cancer survivors in a recent survey reportedly experienced persistent high FCR over the first-year post-surgery. This was associated with lower levels of psychological well-being and quality of life. A manualised intervention (ConquerFear) developed primarily based on the Self-Regulatory Executive Function Model and the Rational Frame Theory, has been found to reduce FCR effectively among Caucasian cancer survivors. The intervention now has been adapted to a Chinese context; ConquerFear-HK. The primary aim of this study is to evaluate its efficacy vs a standard-survivorship-care control (BasicCancerCare) in FCR improvement in a randomised control trial (RCT). METHODS AND ANALYSIS: In this RCT, using the sealed envelope method, 174 eligible Chinese cancer survivors will be randomised to either the ConquerFear-HK or BasicCancerCare intervention. Both interventions include six sessions over 10 weeks, which will be delivered via face to face or online by trained therapists. The ConquerFear-HK intervention incorporates value classification, metacognitive therapy, attentional training, detached mindfulness and psychoeducation; BasicCancerCare includes relaxation training, dietary and physical activity consultations. Participants will be assessed at prior randomisation (baseline; T0), immediately postintervention (T1), 3 months (T2) and 6 months postintervention (T3) on the measures of FCR (Fear of Cancer Recurrence Inventory) as a primary outcome; metacognition (30-item Metacognitions Quesionnaire) and cognitive attentional syndrome (Cognitive-attentional Syndrome Questionnaire) as process outcomes; psychological distress (Hospital Anxiety and Depression Scale), cancer-related distress (Chinese Impact of Events Scale), quality of life (European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire) and treatment satisfaction are secondary outcomes. ETHICS AND DISSEMINATION: Ethics approval has been obtained from HKU/HA HKW Institutional Review Board (ref: UW19-183). The patients/participants provide their written informed consent to participate in this study. The study results will be disseminated through international peer-review publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT04568226.

Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma.

Background: Using high robust radiomic features in modeling is recommended, yet its impact on radiomic model is unclear. This study evaluated the radiomic model's robustness and generalizability after screening out low-robust features before radiomic modeling. The results were validated with four datasets and two clinically relevant tasks. Materials and methods: A total of 1,419 head-and-neck cancer patients' computed tomography images, gross tumor volume segmentation, and clinically relevant outcomes (distant metastasis and local-regional recurrence) were collected from four publicly available datasets. The perturbation method was implemented to simulate images, and the radiomic feature robustness was quantified using intra-class correlation of coefficient (ICC). Three radiomic models were built using all features (ICC > 0), good-robust features (ICC > 0.75), and excellent-robust features (ICC > 0.95), respectively. A filter-based feature selection and Ridge classification method were used to construct the radiomic models. Model performance was assessed with both robustness and generalizability. The robustness of the model was evaluated by the ICC, and the generalizability of the model was quantified by the train-test difference of Area Under the Receiver Operating Characteristic Curve (AUC). Results: The average model robustness ICC improved significantly from 0.65 to 0.78 (P< 0.0001) using good-robust features and to 0.91 (P< 0.0001) using excellent-robust features. Model generalizability also showed a substantial increase, as a closer gap between training and testing AUC was observed where the mean train-test AUC difference was reduced from 0.21 to 0.18 (P< 0.001) in good-robust features and to 0.12 (P< 0.0001) in excellent-robust features. Furthermore, good-robust features yielded the best average AUC in the unseen datasets of 0.58 (P< 0.001) over four datasets and clinical outcomes. Conclusions: Including robust only features in radiomic modeling significantly improves model robustness and generalizability in unseen datasets. Yet, the robustness of radiomic model has to be verified despite building with robust radiomic features, and tightly restricted feature robustness may prevent the optimal model performance in the unseen dataset as it may lower the discrimination power of the model.

A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma.

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.

Real-World Practice of Hypofractionated Radiotherapy in Patients With Invasive Breast Cancer.

PURPOSE: Application of hypofractionated radiotherapy (HFRT) is growing in patients with breast cancer (BC). This study aimed to explore a real-world practice of HFRT in early and locally advanced BC. METHODS: Patients with invasive BC between 2015 and 2019 were retrospectively reviewed. Radiotherapy (RT) was delivered by HFRT and conventionally fractionated radiotherapy (CFRT). Locoregional recurrence-free survival (LRRFS) and disease-free survival (DFS) were calculated by Kaplan-Meier curve and compared by Log-rank test. The effect of treatment modality on DFS was estimated by univariate and multivariable analyses. RESULTS: A total of 1,010 patients were included in this study, and 903 (89.4%) were treated with HFRT. At a median follow-up of 49.5 months, there was no significant difference in a 4-year cumulative incidence of LRRFS in HFRT group (1.5%) and in CFRT group (3.8%) (p = 0.23), neither in different nodal stages nor in N2-3 patients with different molecular subtypes. The 4-year DFS was 93.5% in HFRT group compared with 89.9% in CFRT group with no significant difference either (p = 0.17). Univariate and multivariable analyses also showed no significant difference in DFS between HFRT and CFRT group. However, DFS of HFRT group tended to be lower in N2-3 patients with triple negative BC compared with that of CFRT group (76.2% versus 100%). CONCLUSION: HFRT can achieve similar cumulative incidence of LRRFS and DFS in patients with BC after lumpectomy or mastectomy, and also in different nodal stage, and in locally advanced stage with different molecular subtypes.

Virtual Contrast-Enhanced Magnetic Resonance Images Synthesis for Patients With Nasopharyngeal Carcinoma Using Multimodality-Guided Synergistic Neural Network.

PURPOSE: To investigate a novel deep-learning network that synthesizes virtual contrast-enhanced T1-weighted (vceT1w) magnetic resonance images (MRI) from multimodality contrast-free MRI for patients with nasopharyngeal carcinoma (NPC). METHODS AND MATERIALS: This article presents a retrospective analysis of multiparametric MRI, with and without contrast enhancement by gadolinium-based contrast agents (GBCAs), obtained from 64 biopsy-proven cases of NPC treated at Hong Kong Queen Elizabeth Hospital. A multimodality-guided synergistic neural network (MMgSN-Net) was developed to leverage complementary information between contrast-free T1-weighted and T2-weighted MRI for vceT1w MRI synthesis. Thirty-five patients were randomly selected for model training, whereas 29 patients were selected for model testing. The synthetic images generated from MMgSN-Net were quantitatively evaluated against real GBCA-enhanced T1-weighted MRI using a series of statistical evaluating metrics, which include mean absolute error (MAE), mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Qualitative visual assessment between the real and synthetic MRI was also performed. Effectiveness of our MMgSN-Net was compared with 3 state-of-the-art deep-learning networks, including U-Net, CycleGAN, and Hi-Net, both quantitatively and qualitatively. Furthermore, a Turing test was performed by 7 board-certified radiation oncologists from 4 hospitals for assessing authenticity of the synthesized vceT1w MRI against the real GBCA-enhanced T1-weighted MRI. RESULTS: Results from the quantitative evaluations demonstrated that our MMgSN-Net outperformed U-Net, CycleGAN and Hi-Net, yielding the top-ranked scores in averaged MAE (44.50 ± 13.01), MSE (9193.22 ± 5405.00), SSIM (0.887 ± 0.042), and PSNR (33.17 ± 2.14). Furthermore, the mean accuracy of the 7 readers in the Turing tests was determined to be 49.43%, equivalent to random guessing (ie, 50%) in distinguishing between real GBCA-enhanced T1-weighted and synthetic vceT1w MRI. Qualitative evaluation indicated that MMgSN-Net gave the best approximation to the ground-truth images, particularly in visualization of tumor-to-muscle interface and the intratumor texture information. CONCLUSIONS: Our MMgSN-Net was capable of synthesizing highly realistic vceT1w MRI that outperformed the 3 comparable state-of-the-art networks.

Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy.

PURPOSE: To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC). METHODS AND MATERIALS: Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t-test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models. RESULTS: The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models. CONCLUSIONS: Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models.

A cross-sectional study on the attitudes and perceptions of outpatients towards palliative care at the Hong Kong Queen Mary Hospital Hospice Centre.

BACKGROUND: Palliative care aims to improve the quality of life for patients and their families, by helping them to cope with problems associated with illness. It targets four aspects of health: physical, psychological, social, and spiritual. Most of the current literature on palliative care is limited to the perspectives of health professionals. This study aims to investigate the views of outpatients receiving palliative care at the Hong Kong Queen Mary Hospital Hospice Centre (HKQMHHC), which offers palliative care services to cancer patients. METHODS: This observational cross-sectional study was performed with the completion of a single paper- based original questionnaire over 18 afternoon clinic sessions on Thursdays and Fridays from December 2017 to February 2018 at the HKQMHHC. The questionnaire was designed to examine patients' perspectives; in particular, the Edmonton Symptom Assessment Scale (ESAS) was used to assess their symptoms. Descriptive and univariate analyses were performed. RESULTS: One hundred patients attending HKQMHHC were included in the study. The study revealed that all the mean scores for aspects of care offered at the centre were above 8, on a scale of 0-10 with 0 being extremely inadequate and 10 being extremely adequate. Each respondent was able to identify an average of 1.82 of the 4 aspects of palliative care. Eighty-seven percent of respondents perceived the physical aspect of this care to be of the highest priority. A negative correlation (P<0.05) was found between the extent of symptoms experienced by the patient and their satisfaction towards the services offered. CONCLUSIONS: Patients generally held very positive attitudes, reflecting that the services sufficiently met their needs. However, owing to their rather limited knowledge, this may have restricted their perspectives to a largely superficial level, as many discerned palliative care to be simply targeting physical health with medical consultations. Considering the implications of the results, the addition of accessibility and education components to Hong Kong's current system of palliative care is crucial in the betterment of such services for patients. There should also be increased local coverage of palliative care services to facilitate convenience of access. With reference to the World Health Organisation (WHO) palliative care model, the inclusion of a continued spectrum of services, such as physical and mental health activities and psychosocial counselling, should be reinforced throughout the progression of disease so as to better help patients to cope with illness. The discovery of the relationship between extent of symptoms experienced and patients' satisfaction towards services provided is a new direction for further study.

Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials.

In recent years, new radiotherapy techniques have emerged that aim to improve treatment outcome and reduce toxicity. The standard method of evaluating such techniques is to conduct large scale multicenter clinical trials, often across continents. A major challenge for such trials is quality assurance to ensure consistency of treatment across all participating centers. Analyses from previous studies have shown that poor compliance and protocol violation have a significant adverse effect on treatment outcomes. The results of the clinical trials may, therefore, be confounded by poor quality radiotherapy. Target volume delineation (TVD) is one of the most critical steps in the radiotherapy process. Many studies have shown large inter-observer variations in contouring, both within and outside of clinical trials. High precision techniques, such as intensity-modulated radiotherapy, image-guided brachytherapy, and stereotactic radiotherapy have steep dose gradients, and errors in contouring may lead to inadequate dose to the tumor and consequently, reduce the chance of cure. Similarly, variation in organ at risk delineation will make it difficult to evaluate dose response for toxicity. This article reviews the literature on TVD variability and its impact on dosimetry and clinical outcomes. The implications for quality assurance in clinical trials are discussed.