The Assessment of White Matter Integrity Alteration Pattern in Patients with Brain Tumor Utilizing Diffusion Tensor Imaging: A Systematic Review.

Alteration in the surrounding brain tissue may occur in the presence of a brain tumor. The present study aims to assess the characteristics and criteria of the pattern of white matter tract microstructure integrity alteration in brain tumor patients. The Scopus, PubMed/Medline, and Web of Science electronic databases were searched for related articles based on the guidelines established by PRISMA. Twenty-five studies were selected on the morphological changes of white matter tract integrity based on the differential classification of white matter tract (WMT) patterns in brain tumor patients through diffusion tensor imaging (DTI). The characterization was based on two criteria: the visualization of the tract-its orientation and position-and the DTI parameters, which were the fractional anisotropy and apparent diffusion coefficient. Individual evaluations revealed no absolute, mutually exclusive type of tumor in relation to morphological WMT microstructure integrity changes. In most cases, different types and grades of tumors have shown displacement or infiltration. Characterizing morphological changes in the integrity of the white matter tract microstructures is vital in the diagnostic and prognostic evaluation of the tumor's progression and could be a potential assessment for the early detection of possible neurological defects that may affect the patient, as well as aiding in surgery decision-making.

Remote assessment of cognition and quality of life following radiotherapy for glioma: deep-learning-based predictive models and MRI correlates.

BACKGROUND: Glioma irradiation often unavoidably damages the brain volume and affects cognition. This study aims to evaluate the relationship of remote cognitive assessments in determining cognitive impairment of irradiated glioma patients in relation to the quality of life and MRI changes. METHODS: Thirty patients (16-76 aged) with two imaging (pre- and post-RT) and completed cognitive assessments were recruited. Cerebellum, right and left temporal lobes, corpus callosum, amygdala and spinal cord were delineated and their dosimetry parameters were collected. Cognitive assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE)). Regression models and deep neural network (DNN) were used to evaluate the relationship between brain volume, cognition and treatment dose in patients. RESULTS: Cognitive assessments were highly inter-correlated (r > 0.9) and impairment was shown between pre- and post-RT findings. Brain volume atrophy was shown post-RT, and cognitive impairments were correlated with radiotherapy-associated volume atrophy and dose-dependent in the left temporal lobe, corpus callosum, cerebellum and amygdala. DNN showed a good area under the curve for cognitive prediction; TICS (0.952), T-MoCA (0.909) and Tele-MACE (0.822). CONCLUSIONS: Cognition can be evaluated remotely in which radiotherapy-related brain injury is dose-dependent and volume-dependent. Prediction models can assist in the early identification of patients at risk for neurocognitive decline following RT for glioma, thus facilitating potential treatment interventions.

Remote assessment of cognition and quality of life following radiotherapy for nasopharyngeal carcinoma: deep-learning-based predictive models and MRI correlates.

PURPOSE: Irradiation of the brain regions from nasopharyngeal carcinoma (NPC) radiotherapy (RT) is frequently unavoidable, which may result in radiation-induced cognitive deficit. Using deep learning (DL), the study aims to develop prediction models in predicting compromised cognition in patients following NPC RT using remote assessments and determine their relation to the quality of life (QoL) and MRI changes. METHODS: Seventy patients (20-76 aged) with MRI imaging (pre- and post-RT (6 months-1 year)) and complete cognitive assessments were recruited. Hippocampus, temporal lobes (TLs), and cerebellum were delineated and dosimetry parameters were extracted. Assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE), and QLQ-H&N 43). Regression and deep neural network (DNN) models were used to predict post-RT cognition using anatomical and treatment dose features. RESULTS: Remote cognitive assessments were inter-correlated (r > 0.9). TLs showed significance in pre- and post-RT volume differences and cognitive deficits, that are correlated with RT-associated volume atrophy and dose distribution. Good classification accuracy based on DNN area under receiver operating curve (AUROC) for cognitive prediction (T-MoCA AUROC = 0.878, TICS AUROC = 0.89, Tele-MACE AUROC = 0.919). CONCLUSION: DL-based prediction models assessed using remote assessments can assist in predicting cognitive deficit following NPC RT. Comparable results of remote assessments in assessing cognition suggest its possibility in replacing standard assessments. IMPLICATIONS FOR CANCER SURVIVORS: Application of prediction models in individual patient enables tailored interventions to be provided in managing cognitive changes following NPC RT.

Assessment of gene expressions from squamous cell carcinoma of the head and neck to predict radiochemotherapy-related xerostomia and dysphagia.

PURPOSE: We tested the hypothesis that gene expressions from biopsies of locally advanced head and neck squamous cell carcinoma (HNSCC) patients can supplement dose-volume parameters to predict dysphagia and xerostomia following primary radiochemotherapy (RCTx). MATERIAL AND METHODS: A panel of 178 genes previously related to radiochemosensitivity of HNSCC was considered for nanoString analysis based on tumour biopsies of 90 patients with locally advanced HNSCC treated by primary RCTx. Dose-volume parameters were extracted from the parotid, submandibular glands, oral cavity, larynx, buccal mucosa, and lips. Normal tissue complication probability (NTCP) models were developed for acute, late, and for the improvement of xerostomia grade ≥2 and dysphagia grade ≥3 using a cross-validation-based least absolute shrinkage and selection operator (LASSO) approach combined with stepwise logistic regression for feature selection. The final signatures were included in a logistic regression model with optimism correction. Performance was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS: NTCP models for acute and late xerostomia and the improvement of dysphagia resulted in optimism-corrected AUC values of 0.84, 0.76, and 0.70, respectively. The minimum dose to the contralateral parotid was selected for both acute and late xerostomia and the minimum dose to the larynx was selected for dysphagia improvement. For the xerostomia endpoints, the following gene expressions were selected: RPA2 (cellular response to DNA damage), TCF3 (salivary gland cells development), GBE1 (glycogen storage and regulation), and MAPK3 (regulation of cellular processes). No gene expression features were selected for the prediction of dysphagia. CONCLUSION: This hypothesis-generating study showed the potential of improving NTCP models using gene expression data for HNSCC patients. The presented models require independent validation before potential application in clinical practice.

How many roads must a Malaysian walk down? Mapping the accessibility of radiotherapy facilities in Malaysia.

BACKGROUND: The accessibility to radiotherapy facilities may affect the willingness to undergo treatment. We sought to quantify the distance and travel time of Malaysian population to the closest radiotherapy centre and to estimate the megavoltage unit (MV)/million population based on the regions. MATERIALS & METHODS: Data for subdistricts in Malaysia and radiotherapy services were extracted from Department of Statistics Malaysia and Directory of Radiotherapy Centres (DIRAC). Data from DIRAC were validated by direct communication with centres. Locations of radiotherapy centres, distance and travel time to the nearest radiotherapy were estimated using web mapping service, Google Map. RESULTS: The average distance and travel time from Malaysian population to the closest radiotherapy centre were 82.5km and 83.4mins, respectively. The average distance and travel were not homogenous; East Malaysia (228.1km, 236.1mins), Central (14.4km, 20.1mins), East Coast (124.2km, 108.8mins), Northern (42.9km, 42.8mins) and Southern (36.0km, 39.8mins). The MV/million population for the country is 2.47, East Malaysia (1.76), Central (4.19), East Coast (0.54), Northern (2.40), Southern (2.36). About 25% of the population needs to travel >100 km to get to the closest radiotherapy facility. CONCLUSION: On average, Malaysians need to travel far and long to reach radiotherapy facilities. The accessibility to radiotherapy facilities is not equitable. The disparity may be reduced by adding centres in East Malaysia and the East Coast.

Estimating radiotherapy demands in South East Asia countries in 2025 and 2035 using evidence-based optimal radiotherapy fractions.

BACKGROUND AND PURPOSE: As about 50% of cancer patients may require radiotherapy, the demand of radiotherapy as the main treatment to treat cancer is likely to rise due to rising cancer incidence. This study aims to quantify the radiotherapy demand in countries in Southeast Asia (SEA) in 2025 and 2035 using evidence-based optimal radiotherapy fractions. MATERIALS AND METHODS: SEA country-specific cancer incidence by tumor site for 2015, 2025 and 2035 was extracted from the GLOBOCAN database. We utilized the optimal radiotherapy utilization rate model by Wong et al. (2016) to calculate the optimal number of fractions for all tumor sites in each SEA country. The available machines (LINAC & Co-60) were extracted from the IAEA's Directory of Radiotherapy Centres (DIRAC) from which the number of available fractions was calculated. RESULTS: The incidence of cancers in SEA countries are expected to be 1.1 mil cases (2025) and 1.4 mil (2035) compared to 0.9 mil (2015). The number of radiotherapy fractions needed in 2025 and 2035 are 11.1 and 14.1 mil, respectively, compared to 7.6 mil in 2015. In 2015, the radiotherapy fulfillment rate (RFR; required fractions/available fractions) varied between countries with Brunei, Singapore and Malaysia are highest (RFR > 1.0 - available fractions > required fractions), whereas Cambodia, Indonesia, Laos, Myanmar, Philippines, Timor-Leste and Vietnam have RFR < 0.5. RFR is correlated to GDP per capita (ρ = 0.73, P = 0.01). To allow RFR ≥1 in 2025 and 2035, another 866 and 1177 machines are required, respectively. The number are lower if longer running hours are implemented. CONCLUSION: With the optimal number of radiotherapy fractions, estimation for number of machines required can be obtained which will guide acquisition of machines in SEA countries. RFR is low with access varied based on the economic status.