Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation.

BACKGROUND: Prostate-related quality of life can be assessed with a variety of different questionnaires. The 50-item Expanded Prostate Cancer Index Composite (EPIC) and the International Prostate Symptom Score (IPSS) are two widely used options. The goal of this study was, therefore, to develop and validate a model that is able to convert between the EPIC and the IPSS to enable comparisons across different studies. METHODS: Three hundred forty-seven consecutive patients who had previously received radiotherapy and surgery for prostate cancer at two institutions in Switzerland and Germany were contacted via mail and instructed to complete both questionnaires. The Swiss cohort was used to train and internally validate different machine learning models using fourfold cross-validation. The German cohort was used for external validation. RESULTS: Converting between the EPIC Urinary Irritative/Obstructive subscale and the IPSS using linear regressions resulted in mean absolute errors (MAEs) of 3.88 and 6.12, which is below the respective previously published minimal important differences (MIDs) of 5.2 and 10 points. Converting between the EPIC Urinary Summary and the IPSS was less accurate with MAEs of 5.13 and 10.45, similar to the MIDs. More complex model architectures did not result in improved performance in this study. The study was limited to the German versions of the respective questionnaires. CONCLUSIONS: Linear regressions can be used to convert between the IPSS and the EPIC Urinary subscales. While the equations obtained in this study can be used to compare results across clinical trials, they should not be used to inform clinical decision-making in individual patients. TRIAL REGISTRATION: This study was retrospectively registered on clinicaltrials.gov on January 14th, 2022, under the registration number NCT05192876.

Glioblastoma initiating cells are sensitive to histone demethylase inhibition due to epigenetic deregulation.

Tumor-initiating cells are a subpopulation of cells that have self-renewal capacity to regenerate a tumor. Here, we identify stem cell-like chromatin features in human glioblastoma initiating cells (GICs) and link them to a loss of the repressive histone H3 lysine 9 trimethylation (H3K9me3) mark. Increasing H3K9me3 levels by histone demethylase inhibition led to cell death in GICs but not in their differentiated counterparts. The induction of apoptosis was accompanied by a loss of the activating H3 lysine 9 acetylation (H3K9ac) modification and accumulation of DNA damage and downregulation of DNA damage response genes. Upon knockdown of histone demethylases, KDM4C and KDM7A both differentiation and DNA damage were induced. Thus, the H3K9me3-H3K9ac equilibrium is crucial for GIC viability and represents a chromatin feature that can be exploited to specifically target this tumor subpopulation.

Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices.

PURPOSE: While neural networks gain popularity in medical research, attempts to make the decisions of a model explainable are often only made towards the end of the development process once a high predictive accuracy has been achieved. METHODS: In order to assess the advantages of implementing features to increase explainability early in the development process, we trained a neural network to differentiate between MRI slices containing either a vestibular schwannoma, a glioblastoma, or no tumor. RESULTS: Making the decisions of a network more explainable helped to identify potential bias and choose appropriate training data. CONCLUSION: Model explainability should be considered in early stages of training a neural network for medical purposes as it may save time in the long run and will ultimately help physicians integrate the network's predictions into a clinical decision.

IDH-wildtype glioblastomas and grade III/IV IDH-mutant gliomas show elevated tracer uptake in fibroblast activation protein-specific PET/CT.

PURPOSE: Targeting fibroblast activation protein (FAP) is a new diagnostic approach allowing the visualization of tumor stroma. Here, we applied FAP-specific PET imaging to gliomas. We analyzed the target affinity and specificity of two FAP ligands (FAPI-02 and FAPI-04) in vitro, and the pharmacokinetics and biodistribution in mice in vivo. Clinically, we used 68Ga-labeled FAPI-02/04 for PET imaging in 18 glioma patients (five IDH-mutant gliomas, 13 IDH-wildtype glioblastomas). METHODS: For binding studies with 177Lu-radiolabeled FAPI-02/04, we used the glioblastoma cell line U87MG, FAP-transfected fibrosarcoma cells, and CD26-transfected human embryonic kidney cells. For pharmacokinetic and biodistribution studies, U87MG-xenografted mice were injected with 68Ga-labeled compounds followed by small-animal PET imaging and 177Lu-labeled FAPI-02/04, respectively. Clinical PET/CT scans were performed 30 min post intravenous administration of 68Ga-FAPI-02/04. PET and MRI scans were co-registrated. Immunohistochemistry was done on 14 gliomas using a FAP-specific antibody. RESULTS: FAPI-02 and FAPI-04 showed high binding specificity to FAP. FAPI-04 demonstrated higher tumor accumulation and delayed elimination compared with FAPI-02 in preclinical studies. IDH-wildtype glioblastomas and grade III/IV, but not grade II, IDH-mutant gliomas showed elevated tracer uptake. In glioblastomas, we observed spots with increased uptake in projection on contrast-enhancing areas. Immunohistochemistry showed FAP-positive cells with mainly elongated cell bodies and perivascular FAP-positive cells in glioblastomas and an anaplastic IDH-mutant astrocytoma. CONCLUSIONS: Using FAP-specific PET imaging, increased tracer uptake in IDH-wildtype glioblastomas and high-grade IDH-mutant astrocytomas, but not in diffuse astrocytomas, may allow non-invasive distinction between low-grade IDH-mutant and high-grade gliomas. Therefore, FAP-specific imaging in gliomas may be useful for follow-up studies although further clinical evaluation is required.

The Anonymous Data Warehouse: A Hands-On Framework for Anonymizing Data From Digital Health Applications.

The digital health space is growing rapidly, and so is the interest in sharing anonymized health data. However, data anonymization techniques have yet to see much coverage in the medical literature. The purpose of this article is, therefore, to provide a practical framework for anonymization with a focus on the unique properties of data from digital health applications. Literature trends, as well as common anonymization techniques, were synthesized into a framework that considers the opportunities and challenges of digital health data. A rationale for each design decision is provided, and the advantages and disadvantages are discussed. We propose a framework based on storing data separately, anonymizing the data where the identified data is located, only exporting selected data, minimizing static attributes, ensuring k-anonymity of users and their static attributes, and preventing defined metrics from acting as quasi-identifiers by using aggregation, rounding, and capping. Data anonymization requires a pragmatic approach that preserves the utility of the data while minimizing reidentification risk. The proposed framework should be modified according to the characteristics of the respective data set.

Discontinuation of Palliative Brain Radiotherapy in Patients with Brain Metastases: A Case-Control Study.

Background: Discontinuation of radiotherapy is rarely discussed in the scientific literature. The goal of this study was, therefore, to estimate the frequency of and reasons for treatment discontinuations in patients receiving radiotherapy for brain metastases from solid tumors and to identify factors predicting said discontinuations. Methods: All patients treated for brain metastases from solid tumors between 2010 and 2020 at our institution were retrospectively reviewed. In addition to collecting relevant patient characteristics, the Recursive Partitioning Analysis (RPA) and disease-specific Graded Prognostic Assessment (GPA) groups for each patient were calculated to assess the performance of these scores in predicting treatment discontinuations. Results: Out of 468 patients who underwent cranial radiotherapy, 35 treatments (7.5%) were discontinued. The most frequent reason was clinical deterioration, which was documented in 26 (74.3%) of discontinued treatments. Patients whose radiotherapy was discontinued had, on average, more leptomeningeal disease (20.0% vs. 12.6%), worse ECOG performance status (mean ECOG performance status 1.86 vs. 1.39), and more uncontrolled extracranial metastases (85.3% vs. 70.8%). The frequencies of treatment discontinuation increased with worse prognosis and differed significantly across RPA groups (p = 0.037) but not across GPA groups (p = 0.612). Conclusions: Treatment discontinuation occurred in 7.5% of cases, mostly due to clinical deterioration. Poor performance status, as well as more advanced disease and, in turn, poor prognosis, were associated with higher discontinuation rates.

Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review.

PURPOSE: Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD: The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS: Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS: The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.

The Apple Watch spO2 sensor and outliers in healthy users.

It is unclear how frequently the Apple Watch produces spO2 measurements outside of the normal range in healthy individuals at rest. We conducted a head-to-head comparison in 38 healthy individuals between two watches and two medical-grade pulse oximeters. Fourteen percent of watch measurements yielded spO2 values below 95%, with no values below 92%. Results suggest that outliers measured by the watch should not be a cause for concern in otherwise healthy individuals.

Accuracy of the Apple Watch Oxygen Saturation Measurement in Adults: A Systematic Review.

The purpose of this review is to summarize the research on the accuracy of oxygen saturation (spO2) measurements using the Apple Watch (Apple Inc., Cupertino, California). The Medline and Google Scholar databases were searched for papers evaluating the spO2 measurements of the Apple Watch vs. any kind of ground truth and records were analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The five publications with 973 total patients that met the inclusion criteria all used the Apple Watch Series 6 and described 95% limits of agreement of +/- 2.7 to 5.9% spO2. However, outliers of up to 15% spO2 were reported. Only one study had patient-level data uploaded to a public repository. The Apple Watch Series 6 does not show a strong systematic bias compared to conventional, medical-grade pulse oximeters. However, outliers do occur and should not cause concern in otherwise healthy individuals. The impact of race on measurement accuracy should be investigated.

Re-irradiation for head and neck cancer: outcome and toxicity analysis using a prospective single institution database.

Purpose: Re-irradiation (re-RT) in head and neck cancer is challenging. This study prospectively explored the feasibility of re-RT in patients with loco-regionally recurrent or second primary head and neck cancer (LRR/SP HNC). Methods: From 2004 to 2021, 61 LRR/SP HNC patients were treated with re-RT, defined as having a second course of RT with curative intent resulting in a cumulative dose of ≥100 Gy in an overlapping volume. Postoperative or definitive dynamic intensity-modulated and/or volumetric modulated re-RT was administered using twice daily hyperfractionation to 60 Gy combined with cisplatin or carboplatin/5-fluorouracil. Overall survival (OS), progression-free survival (PFS), locoregional control (LRC) and distant metastasis control (DMC) were analyzed and prognostic factors evaluated. Toxicity was prospectively recorded and graded. Results: The median follow-up was 9.8 months. In 41 patients (67.1%), complete administration of the intended treatment was not feasible. In 9 patients (15%) re-RT was interrupted prematurely and in other 9, the complete re-RT dose was lower than 60 Gy, and 37 patients (61%) could not receive or complete chemotherapy. Two-year OS, PFS and LRC rates were 19%, 18% and 30%, respectively. 20 patients (33%) received the complete intended treatment, and 1- and 2-year OS rates were 70% and 47%, respectively. Charlson comorbidity index was an important predictor for treatment completion. Multivariate analysis revealed recurrent N stage 0-1, age, chemotherapy administration and re-RT dose of 60 Gy as prognostic factors for clinical outcomes. No grade 5 re-RT-related toxicity was observed. The most common new grade ≥3 acute toxicities were dysphagia (52%) and mucositis (46%). Late toxicity included grade ≥3 dysphagia in 5% and osteoradionecrosis in 10% of evaluable patients, respectively. 6 patients (10%) were alive after 9 years without progression and no late toxicity grade ≥3, except for 2 patients presenting with osteoradionecrosis. Conclusion: Hyperfractionated re-RT with 60 Gy combined with platinum-based chemotherapy was a curative treatment option with acceptable toxicity in LRR/SP patients. Patients with higher comorbidity had a higher probability of failing to receive and complete the intended therapy. Consequently, they derived unsatisfactory benefits from re-RT, highlighting the importance of patient selection.

Dose-Volume Histogram Parameters and Quality of Life in Patients with Prostate Cancer Treated with Surgery and High-Dose Volumetric-Intensity-Modulated Arc Therapy to the Prostate Bed.

INTRODUCTION: Prostate bed radiotherapy (RT) is a major affecter of patients' long-term quality of life (QoL). To ensure the best possible outcome of these patients, dose constraints are key for optimal RT planning and delivery. However, establishing refined dose constraints requires access to patient-level data. Therefore, we aimed to provide such data on the relationship between OAR and gastrointestinal (GI) as well as genitourinary (GU) QoL outcomes of a homogenous patient cohort who received dose-intensified post-operative RT to the prostate bed. Furthermore, we aimed to conduct an exploratory analysis of the resulting data. METHODS: Patients who were treated with prostate bed RT between 2010 and 2020 were inquired about their QoL based on the Expanded Prostate Cancer Index Composite (EPIC). Those (n = 99) who received volumetric arc therapy (VMAT) of at least 70 Gy to the prostate bed were included. Dose-volume histogram (DVH) parameters were gathered and correlated with the EPIC scores. RESULTS: The median age at the time of prostate bed RT was 68.9 years, and patients were inquired about their QoL in the median 2.3 years after RT. The median pre-RT prostate-specific antigen (PSA) serum level was 0.35 ng/mL. The median duration between surgery and RT was 1.5 years. The median prescribed dose to the prostate bed was 72 Gy. A total of 61.6% received prostate bed RT only. For the bladder, the highest level of statistical correlation (p < 0.01) was seen for V10-20Gy, Dmean and Dmedian with urinary QoL. For bladder wall, the highest level of statistically significant correlation (p < 0.01) was seen for V5-25Gy, Dmean and Dmedian with urinary QoL. Penile bulb V70Gy was statistically significantly correlated with sexual QoL (p < 0.05). A larger rectal volume was significantly correlated with improved bowel QoL (p < 0.05). Sigmoid and urethral DVH parameters as well as the surgical approach were not statistically significantly correlated with QoL. CONCLUSION: Specific dose constraints for bladder volumes receiving low doses seem desirable for the further optimization of prostate bed RT. This may be particularly relevant in the context of the aspiration of establishing focal RT of prostate cancer and its local recurrences. Our comprehensive dataset may aid future researchers in achieving these goals.

Applications of Machine Learning in Palliative Care: A Systematic Review.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.

Cranial Radiotherapy for Prostate Cancer Patients With Brain Metastases Inaccessible to Stereotactic Radiotherapy.

BACKGROUND/AIM: To evaluate patients and treatment characteristics as well as clinical outcome in patients with intracranial metastases from prostate cancer (PCA) treated with palliative radiotherapy. PATIENTS AND METHODS: Fifteen patients treated for intracranial metastases of PCA were identified. The median age of patients was 69 years. 80% of patients received whole brain radiotherapy and 20% received partial brain radiotherapy. Clinical outcome was assessed. Univariate analysis was performed to analyze the impact of patient specific parameters on survival. RESULTS: There was no >G2 acute or any late toxicity. Median time from the first diagnosis of PCA to first diagnosis of intracranial metastases was 62 months (range=15-160 months). Median survival from first diagnosis of intracranial metastases was 14 weeks (range=0-126 weeks) and 6 weeks (range=0-47 weeks) from the start of radiotherapy. In univariate analysis, survival was significantly better for patients with an Eastern Cooperative Oncology Group (ECOG) performance status 1 compared to ECOG 2-3 [18 weeks (range=5-47 weeks) vs. 3 weeks (range=0-21 weeks), p=0.030] and Recursive Partitioning Analysis (RPA) class 2 compared to RPA class 3 [18 weeks (range=5-47 weeks) vs. 6 weeks (range=0-21 weeks), p=0.045]. CONCLUSION: Overall survival of the patients with wide-spread intracranial metastases from PCA was poor. The decision for a radiotherapy should be done on individual patient basis.

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.

Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

Emerging Role of Carbon Ion Radiotherapy in Reirradiation of Recurrent Head and Neck Cancers: What Have We Achieved So Far?

Administering reirradiation for the treatment of recurrent head and neck cancers is extremely challenging. These tumors are hypoxic and radioresistant and require escalated radiation doses for adequate control. The obstacle to delivering this escalated dose of radiation to the target is its proximity to critical organs at risk (OARs) and possible development of consequent severe late toxicities. With the emergence of highly sophisticated technologies, intensity-modulated radiotherapy (IMRT) and stereotactic body radiotherapy have shown promising outcomes. Proton beam radiotherapy has been used for locally recurrent head and neck cancers because of its excellent physical dose distribution, exploring sharp Bragg peak properties with negligible entrance and exit doses. To further improve these results, carbon ion radiotherapy (CIRT) has been explored in several countries across Europe and Asia because of its favorable physical properties with minimal entrance and exit doses, sharper lateral penumbra, and much higher and variable relative biological efficacy, which cannot be currently achieved with any other form of radiation. Few studies have described the role of CIRT in recurrent head and neck cancers. In this article, we have discussed the different aspects of carbon ions in reirradiation of recurrent head and neck cancers, including European and Asian experiences, different dose schedules, dose constraints of OARs, outcomes, and toxicities, and a brief comparison with proton beam radiotherapy and IMRT.

Retrospective Analysis of Emotional Burden and the Need for Support of Patients and Their Informal Caregivers after Palliative Radiation Treatment for Brain Metastases.

Cancer burdens not only the patients themselves but also their personal environment. A few studies have already focused on the mental health and personal needs of caregivers of patients. The purpose of this retrospective analysis was to further assess the emotional burden and unmet needs for support of caregivers in a population of brain metastasis patients. In the time period 2013-2020, we identified 42 informal caregivers of their respective patients after palliative radiation treatment for brain metastases. The caregivers completed two standardized questionnaires about different treatment aspects, their emotional burden, and unmet needs for support. Involvement of psycho-oncology and palliative care was examined in a chart review. The majority of the caregivers (71.4%, n = 30) suffered from high emotional burden during cancer treatment of their relatives and showed unmet needs for emotional and psychosocial support, mostly referring to information needs and the involvement in the patient's treatment decisions. Other unmet needs referred to handling personal needs and fears of dealing with the sick cancer patient in terms of practical care tasks and appropriate communication. Palliative care was involved in 30 cases and psycho-oncology in 12 cases. There is a high need for emotional and psychosocial support in informal caregivers of cancer patients. There might still be room for an improvement of psychosocial and psycho-oncological support. Care planning should cater to the emotional burden and unmet needs of informal caregivers as well. Further prospective studies in larger samples should be performed in order to confirm this analysis.

Stereotactic Radiotherapy after Radical Prostatectomy in Patients with Prostate Cancer in the Adjuvant or Salvage Setting: A Systematic Review.

(1) Background: Prostate cancer is the most common cancer in men and can be treated with radical prostatectomy (RPE) or radiotherapy in the primary setting. Stereotactic radiotherapy (SBRT) has proven to be effective and well tolerated in this setting. However, if SBRT is an equally promising treatment option if applied in the adjuvant or salvage setting after RPE remains unknown. (2) Methods: We searched the PubMed and Embase databases with the following full-text queries in August 2021 for any combination of the terms "SBRT", "prostate", "adjuvant", "postoperative", "salvage", "stereotactic radiotherapy", "prostate bed". There were no limitations regarding publication date or language. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. (3) Results: We identified 11 individual studies that were included in this systematic review. Three publications included patients without prior radiotherapy and the remaining eight patients with prior radiotherapy. In all but two publications the radiation target was the macroscopic recurrence. SBRT was overall well tolerated with acceptable rates of acute and late gastrointestinal or genitourinary toxicity. Quality of life was published for two phase I trials with good results. There was a very heterogeneous reporting on biochemical control after SBRT. (4) Conclusions: At this point, ultra-hypofractionated RT using SBRT to the prostate bed remains experimental and its use should be restricted to clinical trials. Given the biological rationale for extreme hypofractionation in patients with prostate cancer and the acceptable toxicity rates that have been reported, further exploration of this field is warranted.

Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study.

In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935-0.963) for the internal validation and 0.912 (95% CI 0.866-0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures.

Classifying the Acquisition Sequence for Brain MRIs Using Neural Networks on Single Slices.

Background Neural networks for analyzing MRIs are oftentimes trained on particular combinations of perspectives and acquisition sequences. Since real-world data are less structured and do not follow a standard denomination of acquisition sequences, this impedes the transition from deep learning research to clinical application. The purpose of this study is therefore to assess the feasibility of classifying the acquisition sequence from a single MRI slice using convolutional neural networks. Methods A total of 113 MRI slices from 52 patients were used in a transfer learning approach to train three convolutional neural networks of different complexities to predict the acquisition sequence, while 27 slices were used for internal validation. The model then underwent external validation on 600 slices from 273 patients belonging to one of four classes (T1-weighted without contrast enhancement, T1-weighted with contrast enhancement, T2-weighted, and diffusion-weighted). Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results The neural networks achieved a categorical accuracy of 0.79, 0.81, and 0.84 on the external validation data. The implementation of Grad-CAM showed no clear pattern of focus except for T2-weighted slices, where the network focused on areas containing cerebrospinal fluid. Conclusion Automatically classifying the acquisition sequence using neural networks seems feasible and could be used to facilitate the automatic labelling of MRI data.

Longitudinal Changes of Quality of Life and Hearing Following Radiosurgery for Vestibular Schwannoma.

BACKGROUND: Most existing publications on quality of life (QoL) following stereotactic radiosurgery (SRS) for vestibular schwannomas (VS) gather information retrospectively by conducting surveys several years after treatment. The purpose of this study is therefore to provide longitudinal QoL data and assess how changes in hearing impact QoL. METHODS: Patients completed the 12-item short-form (SF-12) health survey prior to treatment and at every follow-up visit. One hundred and seventy-five patients who had complete forms prior to treatment as well as at an early and at a late follow-up were included in the analysis. For 51 of these patients, longitudinal audiometry data were available. RESULTS: Median follow-up was 7.2 years. Patients experienced a significant reduction in the physical composite score (PCS, p = 0.011) compared to before treatment. The mental composite score (MCS) increased significantly (p = 0.032). A decrease in PCS was not significantly correlated with an increased hearing threshold on the affected but rather on the unaffected ear (r(49) = -0.32, p = 0.023). CONCLUSIONS: It is unclear whether the decline in the PCS is due to treatment-related toxicity or the normal decline of PCS with age. Ensuring proper hearing on the untreated ear might be crucial to ensure good QoL for patients treated with SRS for VS, though this association should be confirmed in additional studies.