Low-level laser therapy/photobiomodulation in the management of side effects of chemoradiation therapy in head and neck cancer: part 2: proposed applications and treatment protocols

Purpose: There is a large body of evidence supporting the efficacy of low-level laser therapy (LLLT), more recently termed photobiomodulation (PBM) for the management of oral mucositis (OM) in patients undergoing radiotherapy for head and neck cancer (HNC). Recent advances in PBM technology, together with a better understanding of mechanisms involved and dosimetric parameters may lead to the management of a broader range of complications associated with HNC treatment. This could enhance patient adherence to cancer therapy, and improve quality of life and treatment outcomes. The mechanisms of action, dosimetric, and safety considerations for PBM have been reviewed in part 1. Part 2 discusses the head and neck treatment side effects for which PBM may prove to be effective. In addition, PBM parameters for each of these complications are suggested and future research directions are discussed. Methods: Narrative review and presentation of PBM parameters are based on current evidence and expert opinion. Results: PBM may have potential applications in the management of a broad range of side effects of (chemo)radiation therapy (CRT) in patients being treated for HNC. For OM management, optimal PBM parameters identified were as follows: wavelength, typically between 633 and 685 nm or 780–830 nm; energy density, laser or light-emitting diode (LED) output between 10 and 150 mW; dose, 2–3 J (J/cm2), and no more than 6 J/cm2 on the tissue surface treated; treatment schedule, two to three times a week up to daily; emission type, pulsed (<100 Hz); and route of delivery, intraorally and/or transcutaneously. To facilitate further studies, we propose potentially effective PBM parameters for prophylactic and therapeutic use in supportive care for dermatitis, dysphagia, dry mouth, dysgeusia, trismus, necrosis, lymphedema, and voice/speech alterations. Conclusion: PBM may have a role in supportive care for a broad range of complications associated with the treatment of HNC with CRT. The suggested PBM irradiation and dosimetric parameters, which are potentially effective for these complications, are intended to provide guidance for well-designed future studies. It is imperative that such studies include elucidating the effects of PBM on oncology treatment outcomes.National Institutes of Health (U.S.) (NIH grant R01AI050875

Low level laser therapy/photobiomodulation in the management of side effects of chemoradiation therapy in head and neck cancer: part 1: mechanisms of action, dosimetric, and safety considerations

Purpose: There is a large body of evidence supporting the efficacy of low level laser therapy (LLLT), more recently termed photobiomodulation (PBM), for the management of oral mucositis (OM) in patients undergoing radiotherapy for head and neck cancer (HNC). Recent advances in PBM technology, together with a better understanding of mechanisms involved, may expand the applications for PBM in the management of other complications associated with HNC treatment. This article (part 1) describes PBM mechanisms of action, dosimetry, and safety aspects and, in doing so, provides a basis for a companion paper (part 2) which describes the potential breadth of potential applications of PBM in the management of side-effects of (chemo)radiation therapy in patients being treated for HNC and proposes PBM parameters. Methods: This study is a narrative non-systematic review. Results: We review PBM mechanisms of action and dosimetric considerations. Virtually, all conditions modulated by PBM (e.g., ulceration, inflammation, lymphedema, pain, fibrosis, neurological and muscular injury) are thought to be involved in the pathogenesis of (chemo)radiation therapy-induced complications in patients treated for HNC. The impact of PBM on tumor behavior and tumor response to treatment has been insufficiently studied. In vitro studies assessing the effect of PBM on tumor cells report conflicting results, perhaps attributable to inconsistencies of PBM power and dose. Nonetheless, the biological bases for the broad clinical activities ascribed to PBM have also been noted to be similar to those activities and pathways associated with negative tumor behaviors and impeded response to treatment. While there are no anecdotal descriptions of poor tumor outcomes in patients treated with PBM, confirming its neutrality with respect to cancer responsiveness is a critical priority. Conclusion: Based on its therapeutic effects, PBM may have utility in a broad range of oral, oropharyngeal, facial, and neck complications of HNC treatment. Although evidence suggests that PBM using LLLT is safe in HNC patients, more research is imperative and vigilance remains warranted to detect any potential adverse effects of PBM on cancer treatment outcomes and survival.National Institutes of Health (U.S.) (grant R01AI050875

What is said or how it is said makes a difference: role of the right fronto-parietal operculum in emotional prosody as revealed by repetitive TMS

Emotional signals in spoken language can be conveyed by semantic as well as prosodic cues. We investigated the role of the fronto-parietal operculum, a somatosensory area where the lips, tongue and jaw are represented, in the right hemisphere to detection of emotion in prosody vs. semantics. A total of 14 healthy volunteers participated in the present experiment, which involved transcranial magnetic stimulation (TMS) in combination with frameless stereotaxy. As predicted, compared with sham stimulation, TMS over the right fronto-parietal operculum differentially affected the reaction times for detection of emotional prosody vs. emotional semantics, showing that there is a dissociation at a neuroanatomical level. Detection of withdrawal emotions (fear and sadness) in prosody was delayed significantly by TMS. No effects of TMS were observed for approach emotions (happiness and anger). We propose that the right fronto-parietal operculum is not globally involved in emotion evaluation, but sensitive to specific forms of emotional discrimination and emotion types

Transforming epilepsy research: A systematic review on natural language processing applications

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision-making. To this end, clinical researchers increasing apply natural language processing (NLP)—a branch of artificial intelligence—as it removes ambiguity, derives context, and imbues standardized meaning from free-narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a “natural language processing” and “epilepsy” query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty-six studies were included. Fifty-eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP-aided quality evaluation for clinical decision-making, outcome prediction, and clinical record summarization

The optimal treatment for patients with stage I non-small cell lung cancer:minimally invasive lobectomy versus stereotactic ablative radiotherapy - a nationwide cohort study

Objectives: The aim of the Early-Stage LUNG cancer (ESLUNG) study was to compare outcomes after minimally invasive lobectomy (MIL) and stereotactic ablative radiotherapy (SABR) in patients with stage I non-small cell lung cancer (NSCLC). Materials and methods: In this retrospective cohort study, patients with clinical stage I NSCLC (according to TNM7), treated in 2014-2016 with MIL or SABR, were included. 5-year overall survival (OS) and recurrence-free survival (RFS) were calculated and compared between patients treated with MIL and a propensity score (PS)weighted SABR population with characteristics comparable to those of the MIL group. Results: 1211 MIL and 972 SABR patients were included. Nodal upstaging occurred in 13.0 % of operated patients. 30-day mortality was 1.0 % after MIL and 0.2 % after SABR. After SABR, the 5-year regional recurrence rate (18.1 versus 14.2 %; HR 0.74, 95 % CI 0.58-0.94) and distant metastasis rate (26.2 versus 20.2 %; HR 0.72, 95 % CI 0.59-0.88) were significantly higher than after MIL, with similar local recurrence rate (13.1 versus 12.1 %; HR 0.90, 95 % CI 0.68-1.19). Unadjusted 5-year OS and RFS were 70.2 versus 40.3 % and 58.0 versus 25.1 % after MIL and SABR, respectively. PS-weighted, multivariable analyses showed no significant difference in OS (HR 0.89, 95 % CI 0.65-1.20) and better RFS after MIL (HR 0.70, 95 % CI 0.49-0.99). Conclusion: OS was not significantly different between stage I NSCLC patients treated with MIL and the PSweighted population of patients treated with SABR. For operable patients with stage I NSCLC, SABR could therefore be an alternative treatment option with comparable OS outcome. However, RFS was better after MIL due to fewer regional recurrences and distant metastases. Future studies should focus on optimization of patient selection for MIL or SABR to further reduce postoperative mortality and morbidity after MIL and nodal failures after SABR

Potential merits and flaws of large language models in epilepsy care: A critical review

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy—combined with the established use of electronic health records—a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations

Transforming epilepsy research: A systematic review on natural language processing applications

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision-making. To this end, clinical researchers increasing apply natural language processing (NLP)—a branch of artificial intelligence—as it removes ambiguity, derives context, and imbues standardized meaning from free-narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a “natural language processing” and “epilepsy” query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty-six studies were included. Fifty-eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP-aided quality evaluation for clinical decision-making, outcome prediction, and clinical record summarization

An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth

Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95% confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies

The optimal treatment for patients with stage I non-small cell lung cancer:minimally invasive lobectomy versus stereotactic ablative radiotherapy - a nationwide cohort study

OBJECTIVES: The aim of the Early-Stage LUNG cancer (ESLUNG) study was to compare outcomes after minimally invasive lobectomy (MIL) and stereotactic ablative radiotherapy (SABR) in patients with stage I non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: In this retrospective cohort study, patients with clinical stage I NSCLC (according to TNM7), treated in 2014-2016 with MIL or SABR, were included. 5-year overall survival (OS) and recurrence-free survival (RFS) were calculated and compared between patients treated with MIL and a propensity score (PS)-weighted SABR population with characteristics comparable to those of the MIL group. RESULTS: 1211 MIL and 972 SABR patients were included. Nodal upstaging occurred in 13.0 % of operated patients. 30-day mortality was 1.0 % after MIL and 0.2 % after SABR. After SABR, the 5-year regional recurrence rate (18.1 versus 14.2 %; HR 0.74, 95 % CI 0.58-0.94) and distant metastasis rate (26.2 versus 20.2 %; HR 0.72, 95 % CI 0.59-0.88) were significantly higher than after MIL, with similar local recurrence rate (13.1 versus 12.1 %; HR 0.90, 95 % CI 0.68-1.19). Unadjusted 5-year OS and RFS were 70.2 versus 40.3 % and 58.0 versus 25.1 % after MIL and SABR, respectively. PS-weighted, multivariable analyses showed no significant difference in OS (HR 0.89, 95 % CI 0.65-1.20) and better RFS after MIL (HR 0.70, 95 % CI 0.49-0.99). CONCLUSION: OS was not significantly different between stage I NSCLC patients treated with MIL and the PS-weighted population of patients treated with SABR. For operable patients with stage I NSCLC, SABR could therefore be an alternative treatment option with comparable OS outcome. However, RFS was better after MIL due to fewer regional recurrences and distant metastases. Future studies should focus on optimization of patient selection for MIL or SABR to further reduce postoperative mortality and morbidity after MIL and nodal failures after SABR

The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research

OBJECTIVE: The quest for epilepsy biomarkers is on the rise. Variables with statistically significant group-level differences are often misinterpreted as biomarkers with sufficient discriminative power. This study aimed to demonstrate the relationship between significant group-level differences and a variable's power to discriminate between individuals. METHODS: We simulated normal-distributed datasets from hypothetical populations with varying sample sizes (25-800), effect sizes (Cohen's d: .25-2.50), and variability (standard deviation: 10-35) to assess the impact of these parameters on significance and discriminative power. The simulation data were illustrated by assessing the discriminative power of a potential real-case biomarker-the EEG beta band power-to diagnose generalized epilepsy, using data from 66 children with generalized epilepsy and 385 controls. Additionally, we evaluated recently reported epilepsy biomarkers by comparing their effect sizes to our simulation-derived effect size criterion. RESULTS: Group size affects significance but not discriminative power. Discriminative power is much more related to variability and effect size. Our real data example supported these simulation results by demonstrating that group-level significance does not translate, one to one, into discriminative power. Although we found a significant difference in the beta band power between children with and without epilepsy, the discriminative power was poor due to a small effect size. A Cohen's d of at least 1.25 is required to reach good discriminative power in univariable prediction modeling. Slightly over 60% of the biomarkers in our literature search met this criterion. SIGNIFICANCE: Rather than statistical significance of group-level differences, effect size should be used as an indicator of a variable's biomarker potential. The minimal required effects size for individual biomarkers-a Cohen's d of 1.25-is large. This calls for multivariable approaches, in which combining multiple variables with smaller effect sizes could increase the overall effect size and discriminative power