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Abstract This is the first case report of a non‐small‐cell lung cancer (NSCLC) patient harboring HIP1‐ALK (H28:A20) and CTNNB1 p.S45del treated with first‐line alectinib. Approximately 5% of NSCLC patients are reported to have anaplastic lymphoma kinase (ALK) rearrangements, and among these EML4‐ALK is the most frequent fusion variant. However, in recent years the use of next‐generation sequencing (NGS) in clinical laboratories has become increasingly widespread, identifying a lot of new ALK fusion partners as well as a large quantity of co‐occurring genomic alterations. Unfortunately, the growing number of genomic alterations detected by NGS does not always correspond to adequate knowledge of their clinical significance, often resulting in an empiric treatment of patients harboring uncommon mutations.

IntroductionMalignant pleural mesothelioma (MPM) is a poor-prognosis disease. Owing to the recent availability of new therapeutic options, there is a need to better assess prognosis. The initial clinical response could represent a useful parameter.MethodsWe proposed a transfer learning approach to predict an initial treatment response starting from baseline CT scans of patients with advanced/unresectable MPM undergoing first-line systemic therapy. The therapeutic response has been assessed according to the mRECIST criteria by CT scan at baseline and after two to three treatment cycles. We used three slices of baseline CT scan as input to the pre-trained convolutional neural network as a radiomic feature extractor. We identified a feature subset through a double feature selection procedure to train a binary SVM classifier to discriminate responders (partial response) from non-responders (stable or disease progression).ResultsThe performance of the prediction classifiers was evaluated with an 80:20 hold-out validation scheme. We have evaluated how the developed model was robust to variations in the slices selected by the radiologist. In our dataset, 25 patients showed an initial partial response, whereas 13 patients showed progressive or stable disease. On the independent test, the proposed model achieved a median AUC and accuracy of 86.67% and 87.50%, respectively.ConclusionsThe proposed model has shown high performance even by varying the reference slices. Novel tools could help to improve the prognostic assessment of patients with MPM and to better identify subgroups of patients with different therapeutic responsiveness.

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a recently described CNS inflammatory disorder that may manifest with optic neuritis, myelitis, seizures, and/or acute disseminated encephalomyelitis. While MOG-specific antibodies in patients with MOGAD are IgG1, a T-cell-dependent antibody isotype, immunologic mechanisms of this disease are not fully understood. Thymic hyperplasia can be associated with certain autoimmune diseases. In this report we describe a case of MOGAD associated with thymic hyperplasia in a young adult.

Digital health technologies (DHTs) can transform neurological assessments, improving quality and continuity of care. In the United States, the Food & Drug Administration (FDA) oversees the safety and efficacy of these technologies, employing a detailed regulatory process that classifies devices based on risk and requires rigorous review and post-market surveillance. Following FDA approval, DHTs enter the Current Procedural Terminology, Relative Value Scale Update Committee, and Centers for Medicare & Medicaid Services coding and valuation processes leading to coverage and payment decisions. DHT adoption is challenged by rapid technologic advancements, an inconsistent evidence base, marketing discrepancies, ambiguous coding guidance, and variable health insurance coverage. Regulators, policymakers, and payers will need to develop better methods to evaluate these promising technologies and guide their deployment. This includes striking a balance between patient safety and clinical effectiveness versus promotion of innovation, especially as DHTs increasingly incorporate artificial intelligence. Data validity, cybersecurity, risk management, societal, and ethical responsibilities should be addressed. Regulatory advances can support adoption of these promising tools by ensuring DHTs are safe, effective, accessible, and equitable.

Background: Sleeve lobectomy with bronchoplasty is a safe surgical technique for the management of lung cancer and endobronchial localization of extrapulmonary cancers. However, anastomotic complications can occur, and treatment strategies are not standardized. Methods: Data from 280 patients subjected to bronchoplasty were retrospectively analyzed, focusing on surgical techniques, anastomotic complications, and their management. Multivariate analysis was performed, and Kaplan–Meier curves were used to determine survival. Results: Ninety percent of 280 surgeries were for lung cancer. Anastomotic complications occurred in 6.42% of patients: late stenosis in 3.92% and broncho-pleural fistula in 1.78%. The median survival was 65.90 months (95% CI = 41.76–90.97), with no difference (p = 0.375) for patients with (51.28 months) or without (71.03 months) anastomotic complications. Mortality at 30 days was higher with anastomotic complications (16.7% vs. 3%, p = 0.014). Multivariable analysis confirmed pathological stage (N+) as a risk factor for anastomotic complications (p = 0.016). Our mortality (3.93%) and morbidity rate (41.78%) corresponded to recent series results. Conclusions: In our experience, surgery is preferred to avoid life-threatening complications in bronchopleural fistulas. Bronchoscopic balloon dilatation is preferred for benign strictures. The nodal stage is related to complications (p = 0.0014), reflecting the aggressiveness of surgery, which requires extended radical lymphadenectomy.

Abstract Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30–55% of recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment of cancer patients. The radiomic analysis of CT images has already shown great potential in solving this task; specifically, Convolutional Neural Networks (CNNs) have already been proposed providing good performances. Recently, Vision Transformers (ViTs) have been introduced, reaching comparable and even better performances than traditional CNNs in image classification. The aim of the proposed paper was to compare the performances of different state-of-the-art deep learning algorithms to predict cancer recurrence in NSCLC patients. In this work, using a public database of 144 patients, we implemented a transfer learning approach, involving different Transformers architectures like pre-trained ViTs, pre-trained Pyramid Vision Transformers, and pre-trained Swin Transformers to predict the recurrence of NSCLC patients from CT images, comparing their performances with state-of-the-art CNNs. Although, the best performances in this study are reached via CNNs with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.91, 0.89, 0.85, 0.90, and 0.78, respectively, Transformer architectures reach comparable ones with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.90, 0.86, 0.81, 0.89, and 0.75, respectively. Based on our preliminary experimental results, it appears that Transformers architectures do not add improvements in terms of predictive performance to the addressed problem.

BackgroundConsolidative thoracic radiotherapy (TRT) has been commonly used in the management of extensive-stage small cell lung cancer (ES-SCLC). Nevertheless, phase III trials exploring first-line chemoimmunotherapy have excluded this treatment approach. However, there is a strong biological rationale to support the use of radiotherapy (RT) as a boost to sustain anti-tumor immune responses. Currently, the benefit of TRT after chemoimmunotherapy remains unclear. The present report describes the real-world experiences of 120 patients with ES-SCLC treated with different chemoimmunotherapy combinations. Preclinical data supporting the hypothesis of anti-tumor immune responses induced by RT are also presented.MethodsA total of 120 ES-SCLC patients treated with chemoimmunotherapy since 2019 in the South of Italy were retrospectively analyzed. None of the patients included in the analysis experienced disease progression after undergoing first-line chemoimmunotherapy. Of these, 59 patients underwent TRT after a multidisciplinary decision by the treatment team. Patient characteristics, chemoimmunotherapy schedule, and timing of TRT onset were assessed. Safety served as the primary endpoint, while efficacy measured in terms of overall survival (OS) and progression-free survival (PFS) was used as the secondary endpoint. Immune pathway activation induced by RT in SCLC cells was explored to investigate the biological rationale for combining RT and immunotherapy.ResultsPreclinical data supported the activation of innate immune pathways, including the STimulator of INterferon pathway (STING), gamma-interferon-inducible protein (IFI-16), and mitochondrial antiviral-signaling protein (MAVS) related to DNA and RNA release. Clinical data showed that TRT was associated with a good safety profile. Of the 59 patients treated with TRT, only 10% experienced radiation toxicity, while no ≥ G3 radiation-induced adverse events occurred. The median time for TRT onset after cycles of chemoimmunotherapy was 62 days. Total radiation dose and fraction dose of TRT include from 30 Gy in 10 fractions, up to definitive dose in selected patients. Consolidative TRT was associated with a significantly longer PFS than systemic therapy alone (one-year PFS of 61% vs. 31%, p

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

Abstract Background The addition of immune checkpoint inhibitors (ICIs) to chemotherapy is the new standard of care in the first‐line treatment of small cell lung cancer (SCLC). However, although the concomitant use of immunotherapy and chemotherapy can increase the antitumor efficacy, it can also increase toxicity. The present study evaluated the tolerability of immune‐based combinations in the first‐line treatment of SCLC. Methods Relevant trials were identified by searching electronic databases and conference meetings. Seven phase II and III randomized controlled trials and 3766 SCLC patients were included in the meta‐analysis (immune‐based combinations = 2133; chemotherapy = 1633). Outcomes of interest included treatment‐related adverse events (TRAEs) and the rate of discontinuation due to TRAEs. Results Immune‐based combination treatment was associated with a higher risk of grade 3–5 TRAEs (odds ratio [OR], 1.16; 95% confidence interval [CI]: 1.01–1.35). Immune‐based combinations were associated with a higher risk of TRAEs leading to discontinuation (OR, 2.30; 95% CI: 1.17–4.54). No differences were observed in grade 5 TRAEs (OR, 1.56; 95% CI: 0.93–2.63). Conclusion This meta‐analysis indicates that the addition of immunotherapy to chemotherapy in SCLC patients is associated with a higher risk of toxicity and probably of treatment discontinuation. Tools for identifying SCLC patients that would not benefit from immune‐based therapy are urgently needed.

Abstract Introduction In STRIVE, natalizumab treatment demonstrated effectiveness in clinical, magnetic resonance imaging (MRI), and patient-reported outcomes (PROs) in patients with early relapsing–remitting multiple sclerosis (RRMS). This post hoc analysis examined the effectiveness and safety of natalizumab in patients who self-identified as either Black/African American (AA) or Hispanic/Latino. Methods Clinical, MRI, and PROs were assessed for the Black/AA subgroup (n = 40) and compared with the non-Hispanic White subgroup (n = 158). As a result of the very small sample size, outcomes for the Hispanic/Latino subgroup (n = 18) were assessed separately, including a sensitivity analysis with Hispanic/Latino patients who completed the 4-year study on natalizumab. Results Clinical, MRI, and PROs were comparable between the Black/AA and non-Hispanic White subgroups except for MRI outcomes at year 1. A higher proportion of non-Hispanic White than Black/AA patients achieved MRI no evidence of disease activity (NEDA; 75.4% vs. 50.0%, p = 0.0121) and no new or newly enlarging T2 lesions (77.6% vs. 50.0%, p = 0.0031) at year 1; these differences were not observed in years 2–4 of the study. For the Hispanic/Latino subgroup in the intent-to-treat population, 46.2% and 55.6% achieved NEDA at years 1 and 2; 66.7% and 90.0% achieved clinical NEDA at years 3 and 4. Annualized relapse rate was reduced by 93.0% at year 1 versus the year before natalizumab initiation; this reduction was maintained throughout the study. Over 4 years, 37.5–50.0% of patients had a clinically meaningful improvement in their Symbol Digit Modalities Test score, and 81.8–100.0% and 90.9–100.0% had stable/improved Multiple Sclerosis Impact Scale-29 physical and psychological scores, respectively. Similar results were observed in the sensitivity analysis with Hispanic/Latino subgroup of the 4-year natalizumab completers. Conclusion These results highlight the effectiveness and safety of natalizumab in patients with early RRMS who self-identified as Black/AA or Hispanic/Latino. ClinicalTrials.gov NCT01485003.

Abstract Background and objectives Recent literature on multiple sclerosis (MS) demonstrates the growing implementation of optical coherence tomography–angiography (OCT-A) to discover potential qualitative and quantitative changes in the retina and optic nerve. In this review, we analyze OCT-A studies in patients with MS and examine its utility as a surrogate or precursor to changes in central nervous system tissue. Methods PubMed and EMBASE were systematically searched to identify articles that applied OCT-A to evaluate the retinal microvasculature measurements in patients with MS. Quantitative data synthesis was performed on all measurements which were evaluated in at least two unique studies with the same OCT-A devices, software, and study population compared to controls. A fixed-effects or random-effects model was applied for the meta-analysis based on the heterogeneity level. Results The study selection process yielded the inclusion of 18 studies with a total of 1552 evaluated eyes in 673 MS-associated optic neuritis (MSON) eyes, 741 MS without optic neuritis (MSNON eyes), and 138 eyes without specification for the presence of optic neuritis (ON) in addition to 1107 healthy control (HC) eyes. Results indicated that MS cases had significantly decreased whole image superficial capillary plexus (SCP) vessel density when compared to healthy control subjects in the analyses conducted on Optovue and Topcon studies (both P

Pragmatic communication abilities refer to the capacity to use language in a social context. Despite evidence to the contrary, the left cerebral hemisphere of the majority of right handers has been considered exclusively specialized for control of language phonology, syntax and semantics, whereas the right hemisphere has been specialized for the control of language pragmatics. Many studies have shown the non-exclusivity of the left hemisphere for language skills. Communication deficits observed in these studies for patients with right hemisphere damage confirmed the necessity for integrity of the right hemisphere across a number of language components. The aim of this study is to investigate the specific role of the right and left hemispheres across several aspects of communication deficits, with particular attention given to the influence of these deficits on functional outcome. The second aim is to characterize possible correlations between pragmatic and other cognitive deficits. We evaluated 22 patients, 15 with left- and 7 with right-brain ischemic or hemorrhagic damage, using cognitive, pragmatic and language tests. We deployed the Right Hemisphere Language Battery–Santa Lucia and Montreal d’Evaluation de la Communication to assess pragmatic abilities. The results showed no statistically significant differences between patients with left- and right-brain damage, highlighting the importance of integration between the two hemispheres in the communication process. Multiple significant correlations were found between pragmatic abilities and cognitive tests assessing global cognitive functioning, pantomime expression and comprehension. Pragmatic deficits were also shown to correlate with functional cognitive outcome. It is important to assess pragmatic abilities in patients with cognitive deficits after both left and right stroke for tailoring neuropsychological intervention to mitigate pragmatic disabilities in functional outcomes.

Recently, the fifth edition of the WHO classification recognized the thoracic SMARCA4-deficient undifferentiated tumor (SMARCA4-UT) as a separate entity from conventional non-small cell lung cancer with SMARCA4 deficiency because of the different clinicopathological characteristics of these two diseases. SMARCA4-UT mainly occurs in young to middle-aged adults and involves a large mass compressing the tissues surrounding the mediastinum and lung parenchyma. Unfortunately, SMARCA4-UT shows a high probability of recurrence after upfront surgery as well as radiotherapy resistance; moreover, chemotherapy has low efficacy. Moreover, given the recent classification of SMARCA4-UT, no data concerning specific clinical trials are currently available. However, several case reports show immunotherapy efficacy in patients with this disease not only in a metastatic setting but also in a neoadjuvant manner, supporting the development of clinical trials. In addition, preclinical data and initial clinical experiences suggest that inhibiting pathways such as CDK4/6, AURKA, ATR, and EZH2 may be a promising therapeutic approach to SMARCA4-UT.

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Renal Cell Carcinoma (RCC) is generally characterized by low-FDG avidity, and [18F]FDG-PET/CT is not recommended to stage the primary tumor. However, its role to assess metastases is still unclear. The aim of this study was to evaluate the diagnostic accuracy of [18F]FDG-PET/CT in correctly identifying RCC lung metastases using histology as the standard of truth. The records of 350 patients affected by RCC were retrospectively analyzed. The inclusion criteria were: (a) biopsy- or histologically proven RCC; (b) Computed Tomography (CT) evidence of at least one lung nodule; (c) [18F]FDG-PET/CT performed prior to lung surgery; (d) lung surgery with histological analysis of surgical specimens; (e) complete follow-up available. A per-lesion analysis was performed, and diagnostic accuracy was reported as sensitivity and specificity, using histology as the standard of truth. [18F]FDG-PET/CT semiquantitative parameters (Standardized Uptake Value [SUVmax], Metabolic Tumor Volume [MTV] and Total Lesion Glycolysis [TLG]) were collected for each lesion. Sixty-seven patients with a total of 107 lesions were included: lung metastases from RCC were detected in 57 cases (53.3%), while 50 lesions (46.7%) were related to other lung malignancies. Applying a cut-off of SUVmax ≥ 2, the sensitivity and the specificity of [18F]FDG-PET/CT in detecting RCC lung metastases were 33.3% (95% CI: 21.4–47.1%) and 26% (95%CI: 14.6–40.3%), respectively. Although the analysis demonstrated a suboptimal diagnostic accuracy of [18F]FDG-PET/CT in discriminating between lung metastases from RCC and other malignancies, a semiquantitative analysis that also includes volumetric parameters (MTV and TLG) could support the correct interpretation of [18F]FDG-PET/CT images.

Abstract Background Potential relationships with the prognosis of patients with extensive‐stage non‐small cell lung cancer (ES‐SCLC) have been investigated without valid results. Methods A retrospective analysis of real‐world data of consecutive patients with ES‐SCLC admitted to our Medical Thoracic Oncology Unit was carried out from 2010 to 2020, focusing on identification of prognostic factors. Kaplan–Meier analysis was used to represent progression‐free survival (PFS) and overall survival (OS). Univariable and multivariable Cox models were used to investigate prognostic factors. Results The analysis included 244 patients. The median OS was 8 months (95% confidence interval [CI]: 8–10) and the median PFS was 5 months (95% CI: 5–6). The univariable analysis showed that factors associated with shorter OS were older age (p = 0.047), TNM stage 4 versus 3 (p 2 metastatic sites (p = 0.004). Mediastinal radiotherapy (RT) (p 1 irradiated site (p = 0.026), 3 and 4 chemotherapy (CT) lines versus 1 (p = 0.044 and 0.001, respectively), prophylactic cranial irradiation (PCI) (p

Abstract Mast cells (MCs) are multifunctional immune cells implicated in both physiological and pathological processes. Among the latter, MCs play a crucial role in cancer. Many studies have shown a correlation between MCs and tumor progression in several solid and hematological malignancies. In particular, MCs can directly promote tumor growth via c‐kit/stem cell factor–dependent signaling and via the release of histamine, which modulate tumor growth through H1 and H2 receptors. At the same time, MCs can increase tumor progression by stimulating angiogenesis via both proangiogenic cytokines stored in their cytoplasm, and by acting on the tumor microenvironment and extracellular matrix. With regard to NSCLC, the role of MCs has not yet been established, with studies showing a correlation with a poor prognosis on the one hand and suggesting a protective effect of MCs on the other hand. These controversial evidences are at least, in part, due to the heterogeneity of the studies exploring the role of MCs in NSCLC, with some studies describing only the MC count without specification of the activation and degranulation state, and without reporting the intratumoral localization and the proximity to other immune and cancer cells. A better knowledge of the role of MCs in NSCLC is mandatory, not only to define their prognostic and predictive proprieties but also because targeting them could be a possible therapeutic strategy.

Abstract Background The diagnosis of multiple sclerosis (MS) requires demyelinating events that are disseminated in time and space. Peripapillary retinal nerve fiber layer (pRNFL) thickness as measured by optical coherence tomography (OCT) distinguishes eyes with a prior history of acute optic neuritis (ON) and may provide evidence to support a demyelinating attack. Objective To investigate whether a deep learning (DL)‐based network can distinguish between eyes with prior ON and healthy control (HC) eyes using peripapillary ring scans. Methods We included 1033 OCT scans from 415 healthy eyes (213 HC subjects) and 510 peripapillary ring scans from 164 eyes with prior acute ON (140 patients with MS). Data were split into 70% training, 15% validation, and 15% test data. We included 102 OCT scans from 80 healthy eyes (40 HC) and 61 scans from 40 ON eyes (31 MS patients) from an independent second center. Receiver operating characteristic curve analyses with area under the curve (AUC) were used to investigate performance. Results We used a dilated residual convolutional neural network for the classification. The final network had an accuracy of 0.85 and an AUC of 0.86, whereas pRNFL only had an AUC of 0.77 in recognizing ON eyes. Using data from a second center, the network achieved an accuracy of 0.77 and an AUC of 0.90 compared to pRNFL, which had an AUC of 0.84. Interpretation DL‐based disease classification of prior ON is feasible and has the potential to outperform thickness‐based classification of eyes with and without history of prior ON.

Background: Since its approval for use, reverse shoulder arthroplasty (RSA) has become the primary treatment for cuff tear arthropathy, with indications expanding more recently to include revision fracture, osteoarthritis with significant glenoid bone loss, tumor, and chronic instability. Instability is a well-described postoperative complication, occurring in 1to 31% of relatively small cohorts and case series. Given the relative infrequency of instability, there remains a need for a comprehensive review of instability with a focus on risk factors and management. Our goal of this systematic review is to describe the prevalence, risk factors, and management strategies for instability following RSA. Methods: A systematic review of the PubMed, EMBASE, MEDLINE, Scopus, and Cochrane Library databases was performed according to PRISMA guidelines. Inclusion criteria included primary RSA cohorts ≥ 100 patients, revision RSA cohorts of any size, and minimum 1-year follow-up. The primary outcome of interest was postoperative instability. MINORS criteria were used to assess study bias. Descriptive statistical analysis was performed with data reported as ranges. Results: Seventeen studies that included 7885 cases of RSA were reviewed. The mean follow-up ranged from 12 to 84 months. Mean age ranged from 64 to 77 years old, and males represented 19 to 39% of cohorts. There were 204 (2.5%) dislocations in 7885 cases, accounting for a rate of instability from 0.4 to 49% across all studies. By intervention, instability rates ranged from 1 to 5% (primary RSA cases), 1 to 49% (revision RSA cases only), and 0.4 to 10% (mixed cohorts). Subscapularis insufficiency and proximal humerus fractures, and fracture sequelae (malunion and nonunion) were identified as risk factors for instability. Closed reduction and casting and revision RSA were reported as successful treatment strategies with acceptable rates of stable prostheses (28-100% and 55-100%, respectively, across studies). Hemiarthroplasty or resection arthroplasty due to recurrent instability was not uncommon after 2 or more episodes of instability. Conclusion: Instability following RSA occurs infrequently (1-5%) following primary RSA and more commonly following revision RSA (1-49%). RSA for acute proximal humerus fracture and fracture sequelae carries a higher risk of instability. Subscapularis repair appears to be a protective factor. While instability may be successfully treated with closed management or revision RSA, recurrent instability may ultimately require hemiarthroplasty or resection arthroplasty.

Background: Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease. Evidence Acquisition: Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI. Results: This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice. Conclusions: We review the current state of AI in neuroophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research. [ABSTRACT FROM AUTHOR]

Mitigating the substantial public health impact of concussion is a particularly difficult challenge. This is partly because concussion is a highly prevalent condition, and diagnosis is predominantly symptom-based. Much of contemporary concussion management relies on symptom interpretation and accurate reporting by patients. These types of reports may be influenced by a variety of factors for each individual, such as preexisting mental health conditions, headache disorders, and sleep conditions, among other factors. This can all be contributory to non-specific and potentially misleading clinical manifestations in the aftermath of a concussion. This review aimed to conduct an examination of the existing literature on emerging approaches for objectively evaluating potential concussion, as well as to highlight current gaps in understanding where further research is necessary. Objective assessments of visual and ocular motor concussion symptoms, specialized imaging techniques, and tissue-based concentrations of specific biomarkers have all shown promise for specifically characterizing diffuse brain injuries, and will be important to the future of concussion diagnosis and management. The consolidation of these approaches into a comprehensive examination progression will be the next horizon for increased precision in concussion diagnosis and treatment.

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.

ObjectivesSuperior sulcus tumour, which affects the lung’s apex, is an uncommon subtype of non-small cell lung cancer (NSCLC). The current study examined the clinical characteristics and management of superior sulcus NSCLC patients in a high-volume referral oncological centre over 22 years.MethodsRetrospective review of 100 surgeries with curative intent for superior sulcus NSCLC over 22 years (July 1998 – December 2020). The surgical approach was defined according to the lesion site and the anatomy of the thoracic inlet. Survival curves, including non-cancer-related deaths, were drawn using the Kaplan-Meier methods, and the log-rank test was used to evaluate differences in survival across groups of patients. Cox proportional hazards regression was used to assess the association between selected clinical and pathologic characteristics on OS.Results54 patients received induction treatments. The surgical approach was anterior thoracotomy in 53 patients, Paulson incision in 30, and a combined in 8. The median postoperative length of stay was 11 days (range: 5 – 27 days). Overall 90-day mortality was 6.93%. The median OS was 24.3 months. After a median follow-up of 3 years, 5-year and 10-year OS rates were 33.9% and 26.4%, respectively. A significantly lower 5-year OS was observed in patients with the nodal disease (46.6% in pN0 vs 13.2% in pN+; p = 0.024), without preoperative treatments (41.0% in patients without preoperative treatments versus 17.4%; p = 0.09) and anteriorly located tumour (anterior vs posterior: 17.4% vs 49.1%; p = 0.032). Cox proportional hazards regression showed better survival in the pT1 stage (HR = 4.6; 95% CI: 1.9 – 11.2; p = 0.00076) and in R0 (HR = 4.2; 95% CI: 1.4 – 12.5; p = 0.010).ConclusionsSuperior sulcus tumours still represent a life-threatening condition that, while curable in a significant proportion of cases, requires complex procedures with high surgical risks and a multimodality treatment setting. An optimal surgical approach should be planned to maximise resection completeness and survival. Other factors affecting survival are related to tumour staging, emphasising the importance of a meticulous preoperative workup and candidate selection to identify those expected to benefit from a survival benefit.

Wolfram syndrome is a neurodegenerative disorder caused by pathogenic variants in the genes WFS1 or CISD2. Clinically, the classic phenotype is composed of optic atrophy, diabetes mellitus type 1, diabetes insipidus, and deafness. Wolfram syndrome, however, is phenotypically heterogenous with variable clinical manifestations and age of onset. We describe four cases of genetically confirmed Wolfram syndrome with variable presentations, including acute-on-chronic vision loss, dyschromatopsia, and tonic pupils. All patients had optic atrophy, only three had diabetes, and none exhibited the classic Wolfram phenotype. MRI revealed a varying degree of the classical features associated with the syndrome, including optic nerve, cerebellar, and brainstem atrophy. The cohort’s genotype and presentation supported the reported phenotype–genotype correlations for Wolfram, where missense variants lead to milder, later-onset presentation of the Wolfram syndrome spectrum. When early onset optic atrophy and/or diabetes mellitus are present in a patient, a diagnosis of Wolfram syndrome should be considered, as early diagnosis is crucial for the appropriate referrals and management of the associated conditions. Nevertheless, the condition should also be considered in otherwise unexplained, later-onset optic atrophy, given the phenotypic spectrum.

Abstract Patients with non‐small cell lung cancer (NSCLC) and uncommon epidermal growth factor receptor (EGFR) mutation are characterized by high heterogeneity, and globally considered to have a worse prognosis than patients with the two common mutations; exon 19 deletion, and exon 21 L858R. Nevertheless, some uncommon mutations do confer sensitivity to tyrosine kinase inhibitors (TKIs) which is comparable with common mutations. In particular, some compound EGFR mutations seem to be characterized by a favorable prognosis. Unfortunately, the rarity of complex EGFR mutations results in difficult clinical decision‐making. Herein, to the best of our knowledge, we report the first case of an NSCLC patient with an EGFR triple mutation containing T785A/L861Q/H297_E298 who was successfully treated with afatinib.

Background Prior to the COVID-19 pandemic, about half of patients from populations that sought care in neurology tried complementary and integrative therapies (CITs). With the increased utilization of telehealth services, we sought to determine whether patients also increased their use of virtual CITs. Methods We examined datasets from two separate cross-sectional surveys that included cohorts of patients with neurological disorders. One was a dataset from a study that examined patient and provider experiences with teleneurology visits; the other was a study that assessed patients with a history of COVID-19 infection who presented for neurologic evaluation. We assessed and reported the use of virtual (and non-virtual) CITs using descriptive statistics, and determined whether there were clinical characteristics that predicted the use of CITs using logistic regression analyses. Findings Patients who postponed medical treatment for non-COVID-19-related problems during the pandemic were more likely to seek CITs. Virtual exercise, virtual psychotherapy, and relaxation/meditation smartphone applications were the most frequent types of virtual CITs chosen by patients. In both studies, age was a key demographic factor associated with mobile/virtual CIT usage. Interpretations Our investigation demonstrates that virtual CIT-related technologies were utilized in the treatment of neurologic conditions during the pandemic, particularly by those patients who deferred non-COVID-related care.