Citations

AbstractBreast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy, particularly with novel antibody‐drug conjugates (ADCs) like trastuzumab deruxtecan, which shows promise for breast cancers with low HER2 expression. Current HER2 testing methods, including immunohistochemistry (IHC) and in situ hybridization (ISH), have limitations. IHC, a semi‐quantitative assay, is prone to interobserver variability. While ISH provides higher precision than IHC, it remains more resource‐intensive in terms of cost and workflow. However, turnaround time is typically faster than that of other advanced molecular methods such as next‐generation sequencing. We adapted the clustering‐constrained‐attention multiple‐instance deep learning model to improve IHC testing and reduce dependence on reflex fluorescence ISH (FISH) tests. Using 5,731 HER2 IHC images, including 592 cases with FISH testing, we trained two models: one for predicting HER2 scores from IHC images and another for predicting FISH scores from equivocal cases. The HER2 IHC score prediction model achieved 91% ± 0.01 overall accuracy and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 ± 0.01. The FISH score prediction model had an ROC AUC of 0.84 ± 0.07, with sensitivity at 0.37 ± 0.13 and specificity at 0.96 ± 0.03. External validation on cases from 203 institutions showed similar performance. The HER2 IHC model maintained a 91% ± 0.01 accuracy and an ROC AUC of 0.98 ± 0.01, while the FISH model had an ROC AUC of 0.75 ± 0.03, with sensitivity at 0.28 ± 0.04 and specificity at 0.93 ± 0.01. Our model advances HER2 scoring by reducing subjectivity and variability in current scoring methods. Despite lower accuracy and sensitivity in the FISH prediction model, it may be a beneficial option for settings where reflex FISH testing is unavailable or prohibitive. With high specificity, our model can serve as an effective screening tool, enhancing breast cancer diagnosis and treatment selection.

ABSTRACTObjectiveThis scoping review explores the multifaceted experience of cancer survivorship in Sub‐Saharan Africa (SSA), with a focus on the post‐treatment phase. The primary objective is to examine the psychosocial, cultural, and economic factors that influence post‐treatment survivorship care and outcomes.MethodsA comprehensive literature review was conducted using databases such as Web of Science Core Collection to identify studies published between 2000 and 2023. Eligible studies focused on post‐treatment cancer survivorship in SSA. Data were extracted, analyzed, and synthesized to identify key themes and research gaps.ResultsThe review identified substantial psychological distress among survivors, including depression, anxiety, and insomnia, often exacerbated by financial toxicity and limited access to psychosocial support services. Cultural factors, such as spiritual beliefs, reliance on traditional healers, and cancer‐related stigma, influenced healthcare‐seeking behaviors and overall well‐being. Despite these challenges, social support networks, religiosity, and targeted psychosocial interventions improved emotional resilience and quality of life. However, major gaps remain, including insufficient integration of cultural beliefs into survivorship care, inadequate long‐term follow‐up (LTFU) programs, limited oncofertility support, and a lack of regionally diverse and longitudinal data.ConclusionsCancer survivorship in SSA is shaped by intricate psychosocial, cultural, and economic dynamics that extend beyond clinical care. Addressing these challenges requires culturally sensitive, evidence‐based interventions, including financial counseling, spiritual care integration, and the establishment of structured LTFU programs. Additionally, expanding access to oncofertility support and integrating culturally relevant psychosocial services can further enhance survivorship outcomes. Strengthening collaboration between policymakers, healthcare providers, and researchers—through interdisciplinary task forces, psycho‐oncology workforce development, and community‐driven initiatives—is essential for improving post‐treatment outcomes and advancing cancer survivorship care in SSA.

AbstractObjectiveStereoelectroencephalography‐guided radiofrequency thermocoagulation (SEEG‐guided RFTC) has been increasingly used as diagnostic and therapeutic approach for drug‐resistant focal epilepsies (DREs). We aimed to describe seizure outcomes of RFTC before and after further neurosurgical intervention.MethodsRetrospective single‐institution case series of patients who underwent SEEG‐RFTC. The primary outcome was Engel class I–IV classification ([responders Engel I–III and non‐responders Engel IV]) at last follow‐up after RFTC and prior to further neurosurgical intervention (open surgical resection, laser ablation, and neuromodulation).ResultsTwenty‐five patients (median age 18.9 years) with DRE were included. The median follow‐up time after RFTC was 7.2 months, including 20 patients who underwent further intervention (median follow‐up time of 7.3 months) and 5 without intervention (median of 3.5 months). From the patients who had further intervention, 17 (85%) underwent surgical procedure (laser ablation 53%, open surgical resection 47%) (median 4.4 months) and 3 (15%) had responsive neurostimulators placed (median 6.9 months). Prior to further intervention (median follow‐up 3.5 months after RFTC), 12 (48%) patients were classified as responders (12% Engel class I, 16% class II, and 20% class III) and 13 (52%) as non‐responders. Following neurosurgical intervention, 17 (68%) patients were followed for a median time of 2.7 months and 3 were lost to follow‐up. Of eight initial RFTC responders, 87.5% and 12.5% had Engel classes I and III, respectively. Of nine who were non‐responders, 33% had Engel class I, 22% II and III, and 44% IV outcomes following further intervention. No neurologic complications were reported.SignificanceSEEG‐guided RFTC is a well‐tolerated procedure and a beneficial diagnostic approach prior to further neurosurgical interventions in patients with DRE.Plain Language SummarySEEG‐guided RFTC is a well‐tolerated procedure and in the patients who experienced initial seizure reduction (n = 8), subsequent neurosurgical intervention (surgery or neuromodulation) led to favorable seizure outcomes (87.5% seizure freedom and 12.5% worthwhile seizure improvement).

SummaryGermline variants in DDX41 (DDX41MT‐germline predisposition syndrome [GPS]) are associated with predisposition to haematological malignancies (HM), including lymphoid and myeloid neoplasms (MN). We retrospectively analysed the clinical and molecular features of 195 patients diagnosed and treated at Mayo Clinic with DDX41MT‐GPS. Patients with germline DDX41 pathogenic variants (42.3%) and variants of unknown significance (VUS, 57.6%) were included. The median age was 68.6 years (16.2–93.4). Ninety‐two per cent were Caucasian, 64.1% were male and 30.8% had a family history of HM. There were 92 distinct germline variants among our cohort, and the most common was p.Met1? (15.9%), followed by p.Asp140Glyfs*2 (9.2%). Clinical diagnoses included asymptomatic carriers (10.2%), clonal cytopenia of undetermined significance (CCUS, 6.1%), myeloproliferative neoplasms (6.7%), myelodysplastic syndrome (40.5%), acute myeloid leukaemia (20.5%), lymphoid neoplasms (9.2%), plasma cell dyscrasias (6.1%) and solid tumours (22.5%). Patients with MN were older (median age 70 vs. 63.5 years) and more likely to be male (M:F ratio 2.3 vs. 1.0) and most patients (78.8%) with MN had a normal karyotype. The most common somatic variants involved DDX41 (34.4%), followed by TET2 (11.2%), DNMT3A (9.6%) and ASXL1 (9.2%). In summary, we have comprehensively described the spectrum of clinical phenotypes within the Mayo Clinic DDX41MT‐GPS cohort.

Background: Medial patellofemoral ligament reconstruction is frequently indicated for recurrent lateral patellar instability. The preoperative presence and severity of a J-sign have been associated with poorer postoperative outcomes. Purpose: To determine the underlying anatomic factors that contribute to the presence, severity, and jumping quality of the J-sign. Study Design: Cross-sectional study; Level of evidence, 3. Methods: All patients undergoing evaluation for patellar instability at a single institution between 2013 and 2023 and healthy controls without patellar instability were included. Patients with a history of knee osteotomies were excluded. The presence of a jumping J-sign and its relationship to patellofemoral measures including the Caton-Deschamps Index (CDI), trochlear dysplasia (Dejour grade), tibial tubercle–trochlear groove (TT-TG) distance, tibial tubercle lateralization, trochlear bump height, mechanical alignment, femoral anteversion, tibial torsion, trochlear medialization, patellar width, axial patellar/trochlear overlap, patellar height, trochlear height, and knee rotation angle (KRA) were measured using standardized 1.5-T magnetic resonance imaging (MRI). Univariate pairwise and multivariable analyses were performed to determine the factors associated with J-sign presence, severity, and quality. Results: Of the 130 knees with patellar instability, 89 (68.5%) demonstrated a J-sign on physical examination. In total, 44 (33.8%) patients demonstrated a 1-quadrant J-sign, 32 (24.6%) demonstrated a 2-quadrant smooth J-sign, and 13 (10.0%) demonstrated a jumping J-sign. A total of 22 control, noninstability cases were included. On multivariable analysis, increasing TT-TG distance (OR, 1.1 increase per millimeter; P = .04), external KRA (OR, 1.1 increase per degree; P = .02), and increasing CDI (OR, 1.3 increase per 0.1 increase in CDI; P = .02) were associated with J-sign presence. Increasing bump height (OR, 1.72 increase per millimeter; P = .007) and decreasing patellar width (OR, 0.89 decrease per millimeter; P = .076) were associated with a larger J-sign, when present. Increasing bump height (OR, 1.80 increase per millimeter; P = .018), increasing patellar width (OR, 1.33 increase per millimeter; P = .047), and decreasing CDI (OR, 0.009 decrease per 0.01 increase in ratio; P = .008) were associated with a jumping J-sign in comparison with a smooth 2-quadrant J-sign. A KRA of 10° (AUC, 0.70) and a cartilaginous bump height of 6.6 mm (AUC, 0.73) were thresholds associated with jumping J-sign presence. Conclusion: The presence of a J-sign is associated with MRI findings of relatively greater external tibiofemoral rotation, increased TT-TG distance, and increased patellar height, while J-sign severity and jumping quality are associated with the presence of additional underlying trochlear factors such as increased bump height. The anatomic drivers identified in this study should be further evaluated as possible factors associated with suboptimal outcomes after surgical management.

ABSTRACT Background Cardiac implantable electronic device (CIED) infections are a serious complication associated with significant morbidity, mortality, and healthcare costs. Despite guideline recommendations for complete device removal, disparities in healthcare access and resource availability between urban and rural settings may influence patient outcomes. This study aims to evaluate rural–urban disparities in the management and outcomes of patients hospitalized with CIED infections. Methods A retrospective cohort analysis was conducted using the National Readmissions Database (NRD) from 2016 to 2021. Patients aged ≥ 18 years hospitalized with CIED infections were identified using ICD‐10 codes. Hospital location was categorized as urban or rural based on the Urban Influence Codes. Baseline characteristics, complications, and outcomes were compared using chi‐square and t ‐tests, and a multivariable logistic regression model was employed to assess the independent association of hospital settings with transvenous lead removal (TLR) utilization. Results A total of 288,402 patients were hospitalized for CIED infections, with 94.9% treated in urban hospitals and 5.1% in rural hospitals. Urban hospital patients had a higher prevalence of key comorbidities, including heart failure, valvular heart disease, atrial fibrillation and peripheral vascular disorders. In‐hospital mortality was significantly higher in urban hospitals (6.2% vs. 4.8%, p < 0.01) likely due to higher burden of comorbidities and higher rates of acute complications such as stroke (3.1% vs. 1.8%, p < 0.01) and systemic embolism (1.4% vs. 0.7%, p < 0.01). TLR was more frequently performed in urban hospitals (20.1% vs. 9.6%, p < 0.01), with rural hospitals exhibiting 59% lower odds of receiving TLR (OR: 0.41, 95% CI: 0.36–0.47, p < 0.01). TLR was associated with reduced in‐hospital mortality, 30‐day mortality, and 30‐day readmission rates across both hospital settings. Conclusion Our study highlights significant rural–urban disparities in CIED infection management. Despite rural hospitals admitting patients with a lower comorbidity burden, TLR utilization was significantly lower, potentially due to limited access to specialized expertise and procedural resources. Given TLR's association with improved survival and reduced readmissions, regardless of the hospital setting, targeted interventions are needed to enhance access to specialized care in rural settings. Further research is warranted to explore strategies for bridging these disparities and optimizing CIED infection outcomes nationwide.

ABSTRACTBackgroundInsomnia and substance use disorders (SUD) are common comorbidities of bipolar disorder (BD). Genome‐wide association studies (GWAS) have uncovered shared genetic contributions to insomnia and BD as well as SUDs and BD. Electronic health record (EHR) derived phenotypes (phecodes) and questionnaire data were used to examine the relationship between insomnia genetic liability and SUDs in BD.Methods40,839 participants from the Mayo Clinic Bipolar Disorder Biobank (BD Biobank; n = 774) and Mayo Clinic Biobank (n = 485 BD cases, n = 39,580 controls) were included in the analyses of diagnosis (phecode) outcomes (insomnia, SUD, alcohol use disorder [AUD] and tobacco use disorder [TUD]). Analyses of specific SUD outcomes obtained through the BD Biobank questionnaire included 1789 cases and considered BD subtype. Logistic regression was used to test for associations between insomnia polygenic risk scores (PRS) and insomnia and SUD outcomes in BD cases and controls.ResultsInsomnia PRS was associated with having an insomnia diagnosis (phecode) in the EHR in controls (OR = 1.19, p = 9.64e‐33) but not in BD cases (OR = 1, p = 0.95). Associations between insomnia PRS and SUD diagnoses were significant in BD cases and controls, with the association being stronger in BD cases (interaction p = 0.024). In the BD Biobank data, the insomnia PRS was associated with increased odds of AUD (OR = 1.19, p = 4.26e‐04), TUD (OR = 1.21, p = 1.25e‐05) and cannabis use disorder (OR = 1.16, p = 4.19e‐03).ConclusionThe effect of genetic predisposition to insomnia on SUD risk may be stronger in BD cases than in controls, which could have clinical care implications for individuals with BD and comorbid SUD.

ABSTRACTIntroductionVon Willebrand factor (VWF) multimer analysis is essential for diagnosing and classifying von Willebrand disease (VWD) but requires expert interpretation and is subject to inter‐rater variability. We developed an automated image analysis pipeline using deep learning to improve the reproducibility and efficiency of VWF multimer pattern classification.MethodsWe trained a YOLOv8 deep learning model on 514 gel images (6168 labeled instances) to classify VWF multimer patterns into 12 classes. The model was validated on 192 images (2304 instances) and tested on an independent set of 94 images (1128 instances). Images underwent preprocessing, including histogram equalization, contrast enhancement, and gamma correction. Two expert raters provided ground truth classifications.ResultsThe model achieved 91% accuracy compared to Expert 1 (macro‐averaged precision = 0.851, recall = 0.757, F1‐score = 0.786) and 87% accuracy compared to Expert 2 (macro‐averaged precision = 0.653, recall = 0.653, F1‐score = 0.641). Inter‐rater agreement was very high between experts (κ = 0.883), with strong agreement between the model and Expert 1 (κ = 0.845) and good agreement with Expert 2 (κ = 0.773). The model performed exceptionally well on common patterns (F1 > 0.93) but showed lower performance on rare subtypes.ConclusionAutomated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.