Citations

8045 Background: Surgical resection is the primary therapy for stage I-II NSCLC but not performed in a subset of patients due to comorbidities or self-election, who instead undergo radiation with or without systemic therapy (RT±ST). Little is known about the demographic, tumoral, therapeutic, and quality-of-life (QOL) characteristics of this non-surgical group. Understanding their differences from the surgical group is essential for developing precision treatment strategies and tailored survivorship guidelines. Methods: In a prospectively enrolled (1997-2016) and followed (through 2025) cohort of 20,951 primary lung cancer patients, 6,486 (31%) had stage I-II NSCLC, including 5,359 (83%) treated surgically (Surgical) and 1,113 (17%) otherwise (Non-surgical). Preexisting diseases were grouped into cancer, lung, and other comorbidities. QOL data were available for 40% of all patients. Descriptive analysis of demographics, tumor features, treatment patterns, and QOL metrics, as well as survival analysis (median years and overall survival [OS] rates) were conducted. QOL measures, on overall and 15 symptom and functional domains, were scored 0 (worst) to 10 (best), with 1-unit difference considered clinically and statistically meaningful. Results: At time of diagnosis, compared to the Surgical, the Non-surgical patients were older (median 73 v 68 years), more likely to have smoked cigarettes (91% v 83%), squamous cell and unspecified NSCLC (53% v 28%), stage IIB (19% v 12%), and preexisting lung comorbidities (71% v 58%). Non-surgical patients more frequently received RT±ST than the Surgical (70% v 21%) and had shorter median survival (2.5 v 7.8) years. Within the Non-surgical, 28% had no cancer treatment (No-Tx), 44% RT-only, 7% ST-only, and 19% both (RT+ST); their respective median diagnosis ages were 74, 76, 68, 70 years; median survival were 1.7, 2.7, 2.2, 3.2 years; 5-year OS (95%CI) at 25% (21-31), 25% (21-29), 26% (17-38), 30% (25-37); and 10-year OS at 9% (7-14), 7% (5-10), 10% (5-21), 9% (6-14), respectively. RT-only group had highest rates of comorbidities (37-84%) and the No-Tx the lowest (31-66%). Although the overall QOL scored the same for all groups, RT-only patients reported the worst scores on multiple symptoms burden, and the No-Tx reported the least pain and lung cancer symptoms but the worst on mental well-being and social activities. Conclusions: Non-surgically managed stage I-II NSCLC cases present clinically distinct patients with the shortest median survival in No-Tx group and highest 5-year survival in RT+ST. RT-only patients were oldest, had the most comorbidities, and reported the worst symptom burdens. These differences highlight the need for more in-depth analyses and studies (considering age, histology, comorbidity, QOL, etc.) to support tailored therapeutic strategies and survivorship care for patients who decline or are not suitable for surgery.

Abstract Background Accurate and efficient dose calculation is essential for online adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results for pencil beam scanning proton therapy (PBSPT) in recent years, but existing DL‐based dose prediction methods still suffer from limited generalizability and an inability to effectively handle outlier clinical cases. This may lead to inaccurate dose delivery to targets or excessive irradiation to organs at risk (OARs), thereby compromising the safety and efficacy of online adaptive proton therapy. Purpose To design a physics‐aware and generalizable AI‐based PBSPT dose prediction method that incorporates underlying physics to enhance generalizability, particularly in handling outlier clinical cases. Methods This study analyzed PBSPT plans of 103 prostate (93 for training and 10 for testing) and 78 lung cancer patients (68 for training and 10 for testing) from our institution, with each case comprising CT images and structure sets. Using the doses generated by our Monte Carlo‐based dose engine as the reference standard, we compared three methods: the region of interest (ROI)‐based method, the beam mask and sliding window method, and the proposed noisy probing dose method, which rapidly generates a low‐statistics dose via uniformly weighted spots on an expanded spot‐placement target volume without optimization. To evaluate the generalizability of these methods to rare treatment planning scenarios, 12 cases with uncommon beam angles or prescription doses were used to assess their performance, which was evaluated using dose‐volume histogram (DVH) indices, 3D Gamma passing rates (3%/2 mm/10%), and Dice coefficients for dose agreement, while prediction times were measured to gauge model efficiency. Results The proposed noisy probing dose method consistently outperformed the ROI‐based and beam mask baselines across all evaluated metrics, with more accurate dose agreement and superior generalizability. For DVH indices, the noisy probing dose method achieved the smallest deviation in clinical target volume (CTV) dose coverage: in prostate cancer, CTV D98 deviation was reduced by 45% (from 0.53 ± 0.22 Gy [RBE] for ROI‐based) and 29% (from 0.41 ± 0.28 Gy [RBE] for beam mask) to 0.29 ± 0.06 Gy [RBE]; in lung cancer, similar improvements were observed, with CTV D98 deviations reduced to 0.34 ± 0.12 Gy [RBE]. The 3D Gamma passing rates improved to 99.65% ± 1.15% for prostate targets and 97.04% ± 1.17% for lung targets. The dice coefficients of the 90% iso‐dose lines were also the highest with the noisy probing dose method (prostate: 0.983 ± 0.005; lung: 0.967 ± 0.01). For the 12 outlier cases, the noisy probing dose method maintained superior generalizability, yielding higher 3D Gamma passing rates (prostate targets: 96.79% ± 0.83%, OARs: 94.29% ± 1.01%; lung targets: 93.38% ± 1.34%, OARs: 93.95% ± 1.32%), demonstrating robust generalizability to rare clinical scenarios. The dose predictions for all testing cases were completed within 0.3 seconds. Conclusions A novel noisy probing dose method was proposed for PBSPT dose prediction in prostate and lung cancer patients. By embedding more proton‐specific physics, this method demonstrated an improvement in the generalizability of dose prediction.

Background: Chronic hip abductor insufficiency remains a challenging problem to treat and can result in significant disability in some patients. Tendon transfers involving the gluteus maximus (GM) or combined GM with tensor fascia latae (GM+TFL) are common options for surgical reconstruction. The purpose of this systematic review was to compare clinical outcomes, complications, and revisions rates of patients who underwent GM transfer or GM+TFL transfer for irreparable hip abductor tears. Methods: PubMed (MEDLINE), Scopus (EMBASE, MEDLINE, COMPENDEX), and Cochrane databases were used to conduct a systematic review of articles from inception to February 2025. A total of 18 studies were included (11 GM transfer, 7 GM+TFL transfer). Demographics, hip setting (native hip, primary total hip arthroplasty [THA], revision THA), patient-reported outcome measures, presence of Trendelenburg gait, abduction strength, complication rates, and revision rates were analysed. Results: A total of 207 patients (208 hips) underwent GM transfer, while 55 patients (55 hips) underwent GM+TFL transfer. Meta-analyses demonstrated that both techniques yielded significant preoperative to postoperative improvements in modified Harris Hip Scores/Harris Hip Scores and Visual Analogue Scale pain scores ( p < 0.001). In total, 33.9% of patients who underwent GM transfer had a persistent Trendelenburg gait postoperatively, compared to 48.1% of those who underwent GM+TFL transfer. The complication rate was 16.8% (23/137) for patients who underwent GM transfer and 2.7% (1/37) for those who underwent GM+TFL transfer; while revision rates were 5.9% (9/152) and 3.6% (2/55), respectively. Conclusions: Both GM and GM+TFL transfers yielded comparable outcomes in patients with chronic hip abductor insufficiency not amendable to primary repair. The complication rate was substantially higher in patients who underwent GM transfer, whereas revision rates were comparably low for both techniques. In particular, a sizeable proportion of patients continued to exhibit a persistent Trendelenburg gait postoperatively following both surgical techniques, highlighting the complexity of this pathology. PROSPERO registration number: CRD420251002466

ABSTRACT Candida auris is an emerging pathogen known for causing invasive, multidrug-resistant infections and healthcare-associated outbreaks. Measuring the relatedness of C. auris strains is critical for detecting and preventing healthcare-associated transmission. Whole genome sequencing (WGS) and analysis of single-nucleotide polymorphisms (SNPs) offer high-resolution assessments of genetic relatedness and antifungal susceptibility; however, interpretation requires an understanding of inter- and intra-patient C. auris diversity and genotypic markers of resistance, respectively. This study aimed to define SNP thresholds for assessing C. auris relatedness in healthcare-associated outbreak investigations, predict antifungal resistance via genotypic markers, and evaluate patient risk factors to inform future surveillance strategies. WGS was performed on 68 C. auris isolates (clades I and III) obtained from 31 hospitalized patients across a healthcare system between 2021 and 2024. Using MycoSNP analysis, we observed a maximum intra-patient variation of 14 SNPs. Five probable transmission clusters were identified based on epidemiologic links, with patient isolates differing by a median of 5 SNPs (range: 0−12). Analysis with a commercial pipeline (refMLST, BugSeq) showed similar clustering patterns. All new detections occurring more than 1 month after admission were linked to a cluster, representing a highly specific indicator of healthcare-associated infection. WGS detected known genotypic markers for fluconazole (ERG11 Y132F, F126L, and MRR1 N647T) and micafungin (FKS1 F635C) resistance, with the latter emerging during antimicrobial therapy. In addition, putative FUR1 mutations (G207R and Q16::STOP) associated with flucytosine resistance were identified. These findings emphasize the utility of WGS for identifying healthcare-associated clusters of C. auris and predicting antifungal resistance. IMPORTANCE Candida auris is a difficult-to-treat yeast that causes invasive infections in vulnerable patient populations. Healthcare exposure is a key risk factor for becoming colonized or infected with C. auris , and infection prevention groups focus on curbing the spread of this organism within the healthcare environment. Whole genome sequencing approaches are key for supporting these efforts, as they can help define clusters of C. auris transmission and can also provide insight into antifungal resistance. Our work provides practical guidance for interpreting genomic data in this setting, helping infection prevention teams respond more effectively to outbreaks and expanding the use of genotypic predictions for antifungal resistance.

Abstract Background Electronic health record (EHR)–based prognostic modeling is increasingly used in oncology, yet incorporating pharmacogenomic (PGx) knowledge derived from experimental systems into clinical prediction frameworks remains challenging. This gap is driven by fundamental mismatches between controlled drug–mutation assays and heterogeneous, incomplete real-world clinical data. Methods We propose a representation transfer framework that integrates PGx embeddings learned from large-scale in vitro pharmacogenomic screens into patient-level EHR models. A frozen pharmacogenomic encoder is used to generate interaction-aware embeddings from patient mutation profiles and administered therapies, which are aggregated into a fixed-length PGx Complementarity Representation. These representations are incorporated into multimodal survival prediction models alongside standard clinical features. Performance was evaluated using systematic modality ablation analyses, attribution analyses, and exploratory unsupervised representation analyses. Results Integrating PGx embeddings yielded consistent performance improvements across all evaluated modality combinations. Relative gains were largest in modality-sparse settings, where baseline EHR features encode limited biological context, and were attenuated—but remained significant—in biologically enriched configurations. Attribution analyses indicated that PGx embeddings contributed non-redundant predictive signal beyond standard clinical features. Exploratory unsupervised analyses further demonstrated that the learned representations exhibit interpretable association patterns aligned with known therapeutic exposures and pathway-level associations. Conclusion These findings suggest that externally learned pharmacogenomic representations can be transferred into real-world EHR models as a context-dependent, non-redundant augmentation. By framing PGx knowledge as an interaction-aware representation rather than a mechanistic model, this work provides an informatics framework for integrating experimental pharmacogenomic data into clinical prediction tasks in a reproducible and interpretable manner.

Abstract BACKGROUND The Response Assessment in Neuro-Oncology (RANO) was recently updated to RANO 2.0, which no longer requires follow-up imaging to confirm progression in recurrent glioma patients recieving immunotherapy, as previously recommended by iRANO. This study evaluates the utility of RANO 2.0 to determine whether radiographic progression on initial post-treatment MRI is sufficient to guide timely discontinuation of ineffective therapy. METHODS This retrospective study, conducted at Mayo Clinic, included patients ≥18 years receiving ≥2 cycles of off-label pembrolizumab for recurrent glioma between 1/1/2014 – 5/1/2025. Radiographic response was assessed using the first MRI performed ≥30 days after pembrolizumab initiation. Progression was defined as a ≥25% increase in the product of perpendicular diameters of the lesion, clear progression of non-measurable disease, new lesion appearance, clinical deterioration, or death. For suspected pseudo-progression, sequential MRIs were reviewed; if progression was confirmed, the original date was used. RESULTS Sixty-two patients were included (mean age: 46.1 years; 64.5% male), receiving pembrolizumab for a median duration of 2.8 (1.4 – 19.4) months. Median time to initial radiographic response assessment was 1.6 (1.1 – 3.5) months, and progression-free survival was 2.3 (0.7 – 15.2) months. Thirty-seven (59.7%) patients were identified with progression at initial evaluation. Among them, 11 were initially suspected of pseudo-progression, and continued pembrolizumab: 3 received additional radiation, 2 initiated bevacizumab, while 6 remained on their prior regimens [pembrolizumab only (3); pembrolizumab with bevacizumab (3)]. However, all were subsequently confirmed to have true progression, with a median delay of 1.4 (0.7 – 7.1) months until pembrolizumab discontinuation. No true pseudo-progression cases were observed. CONCLUSIONS Our findings support the clinical applicability of RANO 2.0 in recurrent glioma patients receiving pembrolizumab. Initial radiographic progression reliably predicted true progression, suggesting limited value in confirmatory follow-up imaging. Early recognition of treatment failure may reduce delays in initiating salvage therapies, along with associated toxicity and cost.

Abstract BACKGROUND Pembrolizumab is FDA approved for solid tumors with high tumor mutation burden (TMB) and may thus have efficacy in hypermutant glioma. This study aimed to identify factors influencing efficacy of pembrolizumab in recurrent glioma. METHODS This retrospective study at Mayo Clinic included patients with adult-type diffuse gliomas who received pembrolizumab (≥2 cycles) for recurrent disease (01/01/2014–05/01/2025). The Mayo Clinic Solid Tumor Panel (MCSTP), which uses the Illumina TruSight Oncology 500 High-Throughput next-generation sequencing (NGS) assay, was also used to assess the molecular characteristics of the recurrent tumors. Responders to pembrolizumab were defined as patients who did not meet RANO 2.0 criteria for progressive disease on first radiographic response assessment. Progression-free survival (PFS) was analyzed using Kaplan-Meier and Cox-Regression analyses. RESULTS Sixty-two patients were identified (median age: 46 years; 65% male; 94% white). All patients received pembrolizumab and had prior alkylating chemotherapy. The median PFS from pembrolizumab initiation was 2.3 months. Twenty-five (40%) patients were responders. Patients with IDH-mutant, 1p/19q co-deleted gliomas (OLIGO, n=13) had a longer PFS (4.3 months) than IDH wildtype gliomas (IDH-WT, n=33, 2.2 months, p=0.012) and IDH-mutant, 1p/19q non-co-deleted (IDH-MUT, n=16, 1.7 months, p=0.0006). The proportion of responders was significantly greater in OLIGO patients (IDH-WT, 33%; IDH-MUT, 19%; OLIGO, 85%, p=0.0008). Overall PFS was not impacted by patient age, sex, or extent of initial resection. Twenty-nine percent of patient tumors tested thus far (6/21) had a high TMB (≥10 mut/Mb). Two-thirds (4/6) of high TMB patients were responders, while only one-third (5/15) of low TMB patients were responders. CONCLUSIONS Patients with recurrent OLIGO may have a longer PFS with pembrolizumab therapy than IDH-WT and IDH-MUT. Furthermore, high TMB may be associated with longer PFS with pembrolizumab therapy in glioma. Additional patients are being assessed using MCSTP (total expected n=43); complete data will be presented.