Treatment Outcomes of Patients with Ependymoma Receiving Radiotherapy: A Single Institution Experience.

INTRODUCTION: The study explored the failure pattern and clinical outcomes in patients with ependymoma undergoing radiotherapy. METHODS: Between January 2004 and June 2022, we included 32 patients with ependymoma who underwent radiotherapy as part of the multimodality treatment at our institution. Of these, 27 (84.4%) underwent adjuvant radiotherapy, four received radiotherapy after local recurrence, and one received definitive CyberKnife radiotherapy (21 Gy in three fractions). The median prescribed dose was 54 Gy in patients who received conventional radiotherapy. We analyzed the local progression-free survival (LPFS), distant metastasis-free survival (DMFS), progression-free survival (PFS), overall survival (OS), and potential prognostic factors. RESULTS: The median age was 29.8 years. Approximately 28.1% were pediatric patients. Fifteen tumors (46.9%) were World Health Organization (WHO) grade II, 10 (31.3%) were WHO grade III, and seven (22.8%) were WHO grade I. Among them, 15 patients (46.9%) had posterior fossa tumors, 10 (31.3%) had supratentorial tumors, and seven (22.8%) had spinal tumors. Of the 31 patients who underwent upfront surgical resection, 19 (61.3%) underwent gross total resection or near total resection. Seventeen of 19 patients with first failures (89.5%) had isolated local recurrences. Of the 19 patients with disease progression, 11 (57.9%) were disease-free or had stable disease after salvage therapy, and five (26.3%) had disease-related mortality. Most of the first local recurrences after radiotherapy occurred in the infield (13 of 16, 81.3%). The 5-year LPFS, DMFS, PFS, and OS rates were 48.5%, 89.6%, 45.1%, and 88.4%, respectively, at a median follow-up of 6.25 years. Subtotal resection was associated with poorer LPFS and PFS in patients with intracranial ependymoma (hazard ratio = 3.69, p = 0.018 for LPFS; hazard ratio = 3.20, p = 0.029 for PFS). CONCLUSION: Incorporating radiotherapy into multimodal treatment has led to favorable outcomes in patients with ependymoma, and the extent of resection is a prognostic factor for the local control of intracranial ependymoma.

Lymphedema microsurgery improved outcomes of pediatric primary extremity lymphedema.

BACKGROUND: Primary lymphedema is an anomaly of the regional lymphatic system with long symptom duration or severe lymphatic obstruction. Few microsurgical treatments for primary lymphedema have been reported. This aim of this study was to investigate the outcomes of microsurgical treatments in pediatric primary lymphedema patients. METHODS: Between 2013 and 2017, pediatric primary lymphedema patients who underwent either lymphovenous anastomosis (LVA) or vascularized lymph node transfer (VLNT) were retrospectively reviewed. Cheng's Lymphedema Grading, Taiwan Lymphoscintigraphy Staging and indocyanine green lymphography were used to select the procedures. No compression garments were used postoperatively. Outcome measurements included circumferential difference, episodes of cellulitis, and Lymphedema-specific Quality of life questionnaire (LYMQoL). RESULTS: Nine patients with mean age of 9.2 years (range, 2-19 years) with 11 lower and two upper lymphedematous limbs underwent 11 VLNT and two LVA. All VLNT flaps survived. At a mean 38.4-months (range, 16-63 months) of follow-up, the mean circumferential difference in nine unilateral lymphedematous limbs was improved by 6.7 ± 9.9% (p = .066). Two patients with bilateral lower limb lymphedema had mean limb circumference improvements of 1.3 and 6.5 cm, respectively. In nine limbs with cellulitis preoperatively, episodes of cellulitis decreased by 2.67 times/year (p = .007). At a mean 22.3-months of follow-up (range, 13-24 months), the LYMQoL overall score in 6 patients older than 7 years was improved by 3.2 ± 1.1 points (p = .007). CONCLUSIONS: Lymphedema microsurgery significantly improved the episodes of cellulitis and quality of life without utilizing compression garments in pediatric primary lymphedema patients.

Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions.

BACKGROUND: We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. RESULTS: We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set. CONCLUSIONS: We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.

Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers.

A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

Revealing cancer subtypes with higher-order correlations applied to imaging and omics data.

BACKGROUND: Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings. METHODS: Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes. RESULTS: In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification. CONCLUSIONS: Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling.

PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.