Recursive Partitioning Research Articles

Quantitative analysis of interstitial lung abnormalities on computed tomography to predict symptomatic radiation pneumonitis after lung stereotactic body radiotherapy

Background and purposeSymptomatic radiation pneumonitis (SRP) is a complication of thoracic stereotactic body radiotherapy (SBRT). As visual assessments pose limitations, artificial intelligence-based quantitative computed tomography image analysis software (AIQCT) may help predict SRP risk. We aimed to evaluate high-resolution computed tomography (HRCT) images with AIQCT to develop a predictive model for SRP. Materials and methodsAIQCT automatically labelled HRCT images of patients treated with SBRT for stage I lung cancer according to lung parenchymal pattern. Quantitative data including the volume and mean dose (Dmean) were obtained for reticulation + honeycombing (Ret + HC), consolidation + ground-glass opacities, bronchi (Br), and normal lungs (NL). After associations between AIQCT’s quantified metrics and SRP were investigated, we developed a predictive model using recursive partitioning analysis (RPA) for the training cohort and assessed its reproducibility with the testing cohort. ResultsOverall, 26 of 207 patients developed SRP. There were significant between-group differences in the Ret + HC, Br-volume, and NL-Dmean in patients with and without SRP. RPA identified the following risk groups: NL-Dmean ≥ 6.6 Gy (high-risk, n = 8), NL-Dmean < 6.6 Gy and Br-volume ≥ 2.5 % (intermediate-risk, n = 13), and NL-Dmean < 6.6 Gy and Br-volume < 2.5 % (low-risk, n = 133). The incidences of SRP in these groups within the training cohort were 62.5, 38.4, and 7.5 %; and in the testing cohort 50.0, 27.3, and 5.0 %, respectively. ConclusionAIQCT identified CT features associated with SRP. A predictive model for SRP was proposed based on AI-detected Br-volume and the NL-Dmean.

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

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

Predictive capacity for local disease control of neogenin-1 (NEO1) transcriptional expression in patients with head and neck squamous cell carcinoma.

To analyze the predictive capacity for local disease control of the transcriptional expression of neogenin-1 (NEO1) gene in patients with head and neck squamous cell carcinoma (HNSCC). A retrospective study was performed on tumor biopsies from 107 patients with HNSCC treated surgically. The transcriptional expression of NEO1 was determined by RT-PCR. NEO1 transcriptional expression value was categorized according to local disease control by recursive partitioning analysis. Lower NEO1 transcriptional expression was associated with worse local control after surgical treatment. Patients with lower NEO1 expression (n = 25, 23.4%) had a 5-year local recurrence-free survival of 61.8% (95% CI: 42.1-81.5%), while patients with higher NEO1 expression (n = 82, 76.6%) had a 5-year local recurrence-free survival of 85.6% (95% CI: 77.6-93.6%), (P = 0.003). According to the result of multivariable analysis, patients with lower NEO1 expression had a 2.7-fold increased risk of local tumor recurrence (95% CI: 1.0-7.0, P = 0.043) compared to patients with higher NEO1 expression. HNSCC patients with a lower transcriptional expression of NEO1 have a significantly higher risk of local recurrence after surgical treatment.

Predictions of Colloidal Molecular Aggregation Using AI/ML Models.

To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits. An analysis of molecular descriptors in favor of colloidal aggregation confirms previous observations (hydrophobicity, molecular weight, and solubility) in addition to undescribed molecular descriptors such as the fraction of sp3 carbon atoms (Fsp3), and electrotopological state of hydroxyl groups (ES_Sum_sOH). Naïve Bayesian modeling and scaffold tree analysis have revealed chemical features/scaffolds contributing the most to colloidal aggregation and nonaggregation, respectively. These results highlight the importance of scaffolds with high Fsp3 values in promoting nonaggregation. Matched molecular pair analysis (MMPA) has also deciphered context-dependent substitutions, which can be used to design nonaggregator molecules. We found that most matched molecular pairs have a neutral effect on aggregation propensity. We have prospectively applied our predictive models to assist in chemical library triage for optimal plate selection diversity and purchase for high throughput screening (HTS) in drug discovery projects.

Association of sarcopenia with severe chemotherapy toxicities and survival in patients with advanced gastric cancer.

Sarcopenia or skeletal muscle depletion is a poor prognostic factor for gastric cancer (GC). However, existing cutoff values of skeletal muscle index (SMI) for defining sarcopenia have been found to have limitations when clinically applied. This study aimed to determine the optimal cutoff for SMI to predict severe toxicities of chemotherapy and overall survival (OS) in patients with advanced GC. Patients with metastatic gastric adenocarcinoma who received first-line palliative chemotherapy between January 2014 and December 2021 at Queen Mary Hospital, Hong Kong, were included in this study. The SMI was determined via a pre-chemotherapy computed tomography scan. Optimal cutoff points of SMI were identified by recursive partitioning analysis. Univariate and multivariate analyses evaluating risk factors of severe chemotherapy toxicities and OS were also performed. A total of 158 patients (male: 108 (68.4%), median age: 65.3) were included. The SMI cutoff to define low SMI was ≤33 cm2/m2 for males and ≤28 cm2/m2 for females; 30 patients (19.0%) had low SMI. Patients with low SMI had a higher incidence of hematological toxicities (63.3% vs 32.0%, P = .001) and non-hematological toxicities (66.7% vs 36.7%, P = .003). Multivariable analysis indicated that low SMI and low serum albumin (≤28 g/L) were independent predictive factors of hematological toxicity, while low SMI and neutrophil-lymphocyte ratio ≥5 were predictive factors of non-hematological toxicity. Moreover, patients with low SMI had a significantly shorter OS (P = .011), lower response rate to chemotherapy (P = .045), and lower utilization of subsequent lines of treatment (P < .001). Using pre-chemotherapy SMI cutoff (≤33 cm2/m2 for males and 28 cm2/m2 for females) one can identify individuals with a higher risk of severe chemotherapy toxicities and worse prognosis.

Body morphometry may predict parastomal hernia following radical cystectomy with ileal conduit.

To investigate whether preoperative body morphometry analysis can identify patients at risk of parastomal hernia (PH), which is a common complication after radical cystectomy (RC). All patients who underwent RC between 2010 and 2020 with available cross-sectional imaging preoperatively and at 1 and 2 years postoperatively were included. Skeletal muscle mass and total fat mass (FM) were determined from preoperative axial computed tomography images obtained at the level of the L3 vertebral body using Aquarius Intuition software. Sarcopenia and obesity were assigned based on consensus definitions of skeletal muscle index (SMI) and FM index (FMI). PH were graded using both the Moreno-Matias and European Hernia Society criteria. Binary logistic regression and recursive partitioning were used to identify patients at risk of PH. The Kaplan-Meier method with log-rank and Cox proportional hazards models included clinical and image-based parameters to identify predictors of PH-free survival. A total of 367 patients were included in the final analysis, with 159 (43%) developing a PH. When utilising binary logistic regression, high FMI (odds ratio [OR] 1.63, P < 0.001) and low SMI (OR 0.96, P = 0.039) were primary drivers of risk of PH. A simplified model that only relied upon FMI, SMI, and preoperative albumin improved the classification of patients at risk of PH. On Kaplan-Meier analysis, patients who were obese or obese and sarcopenic had significantly worse PH-free survival (P < 0.001). Body morphometry analysis identified FMI and SMI to be the most consistent predictors of PH after RC.

Examining early adherence measures as predictors of subsequent adherence in an intensive longitudinal study of individuals in mutual help groups: One day at a time.

Individuals with a substance use disorder complete ecological momentary assessments (EMA) at lower rates than community samples. Previousresearch in tobacco users indicates that early log-in counts to smoking cessation websites predicted subsequent smoking cessation website usage. We extended this line of research to examine individuals who are seeking to change their drinking behaviors through mutual support groups. We examined whether adherence in the first 7 days (1487 observations) of an intensive longitudinal study design could predict subsequent EMA protocol adherence (50% and 80% adherence separately) at 30 (5700 observations) and 60 days (10,750 observations). Participants (n = 132) attending mutual-help groups for alcohol use completed two assessments per day for 6 months. We trained four classification models (logistic regression, recursive partitioning, support vector machines, and neural networks) using a training dataset (80% of the data) with each of the first 7 days' cumulative EMA assessment completion. We then tested these models to predict the remaining 20% of the data and evaluated model classification accuracy. We also used univariate receiver operating characteristic curves to examine the minimal combination of days and completion percentage to best predict subsequent adherence. Different modeling techniques can be used with early assessment completion as predictors to accurately classify individuals that will meet minimal and optimal adherence rates later in the study. Models ranged in their performance from poor to outstanding classification, with no single model clearly outperforming other models. Traditional and machine learning approaches can be used concurrently to examine several methods of predicting EMA adherence based on early assessment completion. Future studies could investigate the use of several algorithms in real time to help improve participant adherence rates by monitoring early adherence and using early assessment completion as features in predictive modeling.

Fragment-based QSAR study to explore the structural requirements of DPP-4 inhibitors: a stepping stone towards better type 2 diabetes mellitus management

ABSTRACT Dipeptidyl peptidase-4 (DPP-4) inhibitors belong to a prominent group of pharmaceutical agents that are used in the governance of type 2 diabetes mellitus (T2DM). They exert their antidiabetic effects by inhibiting the incretin hormones like glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide which, play a pivotal role in the regulation of blood glucose homoeostasis in our body. DPP-4 inhibitors have emerged as an important class of oral antidiabetic drugs for the treatment of T2DM. Surprisingly, only a few 2D-QSAR studies have been reported on DPP-4 inhibitors. Here, fragment-based QSAR (Laplacian-modified Bayesian modelling and Recursive partitioning (RP) approaches have been utilized on a dataset of 108 DPP-4 inhibitors to achieve a deeper understanding of the association among their molecular structures. The Bayesian analysis demonstrated satisfactory ROC values for the training as well as the test sets. Meanwhile, the RP analysis resulted in decision tree 3 with 2 leaves (Tree 3: 2 leaves). This present study is an effort to get an insight into the pivotal fragments modulating DPP-4 inhibition.

Development and validation of a clinical risk score for postoperative outcome in newly diagnosed glioblastoma: A report of the RANO Resect group.

2060 Background: Following resection or biopsy, patients with newly diagnosed glioblastoma frequently enter clinical trials. To standardize terminology for extent of resection, we proposed the RANO classification acknowledging four prognostic resection classes reflecting different amounts of residual tumor. Here, we aim to make use of our classification to (I) analyze the interactive effects of residual tumor with clinical and molecular factors on outcome (II) to define a prognostic postoperative risk score. Methods: The RANO resect group retrospectively compiled an international, seven-center training cohort of newly diagnosed glioblastoma. Predictors of outcome were identified by uni- and multivariate Cox’s proportional hazard regression. The combined effects of residual tumor with prognostic molecular and clinical factors on survival were assessed by recursive partitioning analysis (significance level: p≤ 0.05). Terminal regression tree nodes were combined into risk classes guided by Kaplan-Meier survival analyses and log-rank tests. The risk classes were converted into a numerical score which was prognostically verified in an external validation cohort. Results: Our training cohort compromised 1003 patients with newly diagnosed IDH-wildtype glioblastoma, including 744 patients who underwent adjuvant radiochemotherapy per EORTC-NCIC (TMZ/RT→TMZ). Residual tumor per RANO classification, MGMT promotor methylation status, age (as continuous variable; optimal cutoff: ≤65 years), and KPS (as continuous variable; optimal cutoff: ≥80) were prognostic of overall survival and forwarded into regression tree analysis. By combining eleven terminal nodes and individually weighting the prognostic factors, an additive scale (range, 0-9 points) integrating these four variables distinguished patients with low (0-2 points), intermediate (3-5 points), and high risk (6-9 points) for poor postoperative outcome (median overall survival: 24 vs. 16 vs. 6 months; p &lt; 0.01). Adjustment of the regression tree for adjuvant therapy was applied, and the presence of the three risk groups was confirmed in the subgroups of patients treated with or without RT/TMZ→TMZ. The prognostic value of the risk score was verified in a external single-center validation cohort of newly diagnosed glioblastoma ( n = 231, p &lt; 0.01). When we compared our risk score to previously postulated risk models, our score was superior in concordance between predicted with observed survival (c-index; development cohort: 0.7, validation cohorts: 0.6). Conclusions: The novel RANO risk score integrates molecular and clinical factors to serve as an easy-to-use, yet highly prognostic tool to stratify patients after resection or biopsy of newly diagnosed glioblastoma. The score may serve to guide patient management and reduce imbalances between study arms in prospective interventional trials.

Prognostic factors for patients with breast cancer brain metastasis after stereotactic radiotherapy.

e13109 Background: Breast cancer (BC) is the most common cancer in females and BC brain metastasis (BCBM) is considered as the second most frequent brain metastasis. Numerous prognostic factors are associated with survival for patients with BCBMs. Here we investigated the clinical factors associated with overall survival (OS) and distant intracranial control (DIC) after stereotactic radiotherapy (SRT) for BCBM. Methods: The records of BCBM patients treated with SRT were collected from electronic database in Cancer Center, Union Hospital, Tongji medical college, Huazhong University of Science and Technology Hospital. Based on the univariate and multivariate Cox regression analysis, the prognostic significance of available variables was assessed. The nomogram was created from Cox models and internally validated by use of bootstrap and cross-validation. Model discrimination was measured by calibration plots and the concordance index, and the clinical benefits were evaluated through clinical decision curve analysis (DCA). Results: 101 BCBM patients receiving SRT were analyzed in this study, and 5 patients were excluded with other primary malignancies or missing data. Median observation time was 15.0 months, and median OS was 22.0 months. OS rate was 71.5% in 1 year, 45.8% in 2 years, and 32.8% in 3 years. DIC was 49.8% in 1 year, 26.6% in 2 years, and 18.3% in 3 years. Multivariable analysis identified prognostic factors for OS including number of the brain metastasis, molecular subtypes type, brain metastasis as the first metastasis site, Karnofsky performance status (KPS), and receiving systemic therapy after SRT. KPS&lt;80, triple-negative subtype, the existence of extracranial metastases, the existence of lung metastases were associated with decreased DIC. Selected by the Akaike information criterion (AIC), the nomogram model contains age, KPS, histological subtype, number of brain metastases, brain metastasis as first metastasis site, PTV size, liver metastases, albumin and neutrophils. The calibration curve for probability of survival showed high consistencies between prediction by nomogram and actual observation. The C-index of the nomogram for predicting survival was 0.823, and the bias-corrected C-index generated by bootstrap validation with 1,000 resamples was 0.772, which was statistically higher than the C-index values of the other systems: Recursive partitioning analysis (RPA) (0.627), Graded prognostic assessment (GPA) (0.637), Breast-specific Graded Prognostic Assessment (breast-GPA) (0.699). BCBM patients receiving SRT were divided into four risk groups: I group (risk score &lt; 150), II group (150≤risk score&lt; 220), III group (220≤risk score&lt;290) and IV group (risk score≥290). The Kaplan‐Meier OS curves showed great discrimination among the four risk groups. Conclusions: The proposed nomogram resulted in a more-accurate prognostic prediction for BCBM patients receiving SRT.

Predicting therapeutic clinical trial enrollment for underrepresented patients in neuro-oncology using supervised machine learning.

1614 Background: Despite the unique ability of machine learning (ML) techniques to uncover complex interactive effects in datasets, studies exploring predictors of clinical trial enrollment have largely used standard statistical methods to discern associations. Furthermore, there have been limited applications of ML to close existing disparities in clinical trial enrollment of underrepresented patients in neuro-oncology. This study aims to use supervised machine learning to define and validate primary contributors of therapeutic clinical trial enrollment for a) all patients, b) women and c) NIH-designated minority patients with low- and high-grade glioma. Methods: Adult glioma patients who received care from the UCSF Brain Tumor Center between 1997- 2017 were identified in a prospective registry. Bootstrap forest (BF) and Recursive Partitioning (RP) models were created by randomly dividing patients in a 70/30 split into development (DEV) and validation (VAL) cohorts among all patients, women, and NIH-designated minority patients, separately. Model performance was assessed using the area under the curve (AUC) in the DEV and VAL cohorts. Results: Among 1042 patients, 350 patients (33.6%) enrolled in a therapeutic clinical trial. There were 445 women (42.7%), of which 144 (32.4%) enrolled, and 141 minority patients (13.5%), of which 39 (27.7%) enrolled. For all patients, in order of decreasing influence, the BF model selected median neighborhood household income, age, neighborhood poverty level, distance from hospital, tumor location, tumor volume, treatment with chemotherapy, occupation, KPS, insurance status, employment status, seizure at presentation, and volumetric extent of resection (EOR) to predict trial enrollment (DEV AUC: 0.984; VAL AUC: 0.746). For women, the BF model selected distance from hospital, age, household income, neighborhood poverty level, tumor location, treatment with chemotherapy, and KPS for trial prediction (DEV AUC: 0.972; VAL AUC: 0.746), whereas for minority patients, the BF model selected neighborhood poverty level, age, occupation, distance from hospital, tumor volume, insurance status, employment status, and EOR to predict trial enrollment (DEV AUC: 0.9830; VAL AUC: 0.769). The RP model for women designated those with non-White race as less likely to enroll (DEV AUC: 0.735; VAL AUC: 0.665) while the RP model for minority patients characterized those who preferred a non-English language or who were divorced or widowed as less likely to enroll (DEV AUC: 0.775; VAL AUC: 0.601). Conclusions: Supervised machine learning models achieved similar predictive performance among patient subgroups, while uncovering interactions between gender identity, minority status, and sociodemographic variables. These findings can guide targeted recruiting efforts to advance equitable trial enrollment for women and minorities.

Real-world quality analysis of palliative care utilization in patients with brain metastases at three academic institutes.

12080 Background: Brain metastases (BMETs) are a devastating complication of cancer, occurring in up to 30-50% of patients with advanced solid tumors. Median survival after diagnosis of BMETs can be limited, with 3-6 months survival for patients with poorer performance status (Updated Recursive Partition analysis [U-RPA] classification class 2b and 3). Early integration of palliative care (PC) can improve a patient’s quality of life. Per guidelines, patients with advanced cancer should be referred to an interdisciplinary team, including PC, early in the disease. We aimed to assess the utilization and impact of PC in patients with BMETs at three academic centers. Methods: We obtained data from the electronic medical record (EMR) of all patients diagnosed with BMETS at the University of Virginia, the Medical College of Wisconsin, and the University of Vermont between 1 January 2017 and 31 December 2019. Inclusion criteria were age 18+ years, diagnosis of BMETs secondary to a solid tumor malignancy (excluding leptomeningeal metastases), and &gt;3 months of follow-up treatment. Patients were included if considered at high risk for mortality, which was defined as KPS &lt;70 (U-RPA Class 3) or KPS 70-80 and age &gt; 65 (U-RPA Class 2b). Chi-squared and Fisher’s exact tests compared survival, hospice referral, emergency visits, readmissions, and advance directives (AD) on the file between the PC and non-PC groups. Results: In total, of the 309 patients that were assessed, 220 patients were class 2b, and 89 were class 3 per U-RPA classification. The mean age was 66 in both groups, and 167 (54%) were seen by PC. The median survival for patients seen by PC was 7.8 months compared to 8.3 months (p 0.056). There was no statistically significant difference in survival between the two groups. 80% of patients in the group seen by PC enrolled with hospice care compared to 58% in the non-PC group (p 0.0002). 80% of patients had an AD on file in the group seen by PC compared to 56% in the non-PC group (p &lt;0.0001). In terms of ED visits or re-hospitalization, there was a trend for more ED visits/readmissions in the group seen by PC compared to the non-PC group (p 0.057). Conclusions: In this real-world quality analysis, we found that there is under-utilization of formal PC consultation in patients with BMETs. There was no difference in survival between groups, indicating PC is not synonymous with end-of-life or hospice care. PC was associated with more patients completing ADs and more patients using hospice. Health systems and policy initiatives promoting palliative care utilization earlier in the disease course may support patient values and preferences at the end of life. [Table: see text]

Methylation Analysis to Detect CIN3+ in High-Risk Human Papillomavirus-Positive Self-Samples From the Population-Based Cervical Cancer Screening Program

Since 2017, a self-sampling device has been introduced to the Dutch population-based screening program to enable higher participation rates. However, routine triage cytology cannot be performed on self-sampling material. Methylation is an alternative triage method that can be performed directly on DNA extracted from self-samples. Recently, we tested a set of 15 published cervical intraepithelial neoplasia grade 3 or worse (CIN3+)-specific methylation markers and found a panel of 3 markers with a sensitivity of 82% and a specificity of 74%. In this study, we determined the sensitivity and specificity of 2 commercial assays using quantitative methylation-specific PCR. DNA from the same cohort of high-risk human papillomavirus-positive self-sampled material obtained through the population-based screening program in the North of the Netherlands from women with CIN2 or less (<CIN3, n = 208) and women with CIN3+ (n = 96) was used. The QIAsure methylation test (consisting of 2 methylation markers) showed a sensitivity of 65% and a specificity of 72%, whereas the Gyntect assay (consisting of 6 methylation markers) showed a sensitivity of 59% and a specificity of 91% for CIN3+. When we compared all individual 23 methylation markers, receiver operating characteristic analysis showed an area under the curve of ≥0.7 for 11 of 23 markers (P < .001). By model-based recursive partitioning and robustness analysis, we found a panel with a better sensitivity compared with QIAsure and Gyntect (P < .001). This new panel, consisting of ITGA4, ASCL1, and FAM19A4, has a sensitivity of 84% and a specificity of 70%, similar to our previously identified panel (ANKRD18CP, LHX8, and EPB41L3). Thus, in addition to our previously identified panel, the combination of ITGA4, ASCL1, and FAM19A4 showed good diagnostic performance and potentially can replace cytology, thereby avoiding additional doctor visits for many women worldwide and reducing the time for decision making for referral to the gynecologist.

Local Ablative Therapy Combined With Pembrolizumab in Patients With Synchronous Oligometastatic Non-Small Cell Lung Cancer: A Recursive Partitioning Analysis

Purpose: This study aimed to evaluate the efficacy of local ablative therapy (LAT) combined with pembrolizumab in patients with synchronous oligometastatic non-small cell lung cancer (NSCLC) and to identify patients who would most benefit from LAT.Methods and Materials: We retrospectively identified patients diagnosed with synchronous oligometastatic NSCLC (≤5 metastatic lesions and ≤3 organs involved) and treated with first-line pembrolizumab between January 2017 and December 2022. Patients who underwent LAT, including surgery or radiotherapy at all disease sites, were compared with those who did not undergo LAT. A recursive partitioning analysis (RPA) model was developed using prognostic factors for progression-free survival (PFS).Results: Among the 258 patients included, 78 received LAT with pembrolizumab and 180 received pembrolizumab alone. The median follow-up duration was 15.5 months (range, 3.0–71.2). In the entire cohort, LAT was independently associated with significantly improved PFS (hazard ratio [HR], 0.64; P = 0.015) and overall survival (OS) (HR, 0.61; P = 0.020). In the propensity score-matched cohort (N = 74 in each group), the median PFS was 19.9 months and 9.6 months, respectively (P = 0.003), and the median OS was 42.2 months and 20.5 months, respectively (P = 0.045), for the LAT and non-LAT groups. Based on the RPA model, incorporating the number of metastatic lesions, performance status, and PD-L1 expression level, patients were stratified into three risk groups with distinct PFS. LAT significantly improved PFS and OS in the low- and intermediate-risk groups; however, no difference was observed in the high-risk group. LAT was more effective as a consolidative treatment following pembrolizumab initiation than as an upfront therapy.Conclusion: LAT combined with pembrolizumab was associated with higher PFS and OS compared to pembrolizumab alone in selected patients with synchronous oligometastatic NSCLC. The RPA model could serve as a valuable clinical tool for identifying appropriate patients for LAT.

Intensity-Modulated Reirradiation Therapy With Nivolumab in Recurrent or Second Primary Head and Neck Squamous Cell Carcinoma

Intensity-modulated radiation therapy (IMRT) reirradiation of nonmetastatic recurrent or second primary head and neck squamous cell carcinoma (HNSCC) results in poor progression-free survival (PFS) and overall survival (OS). To investigate the tolerability, PFS, OS, and patient-reported outcomes with nivolumab (approved standard of care for patients with HNSCC) during and after IMRT reirradiation. In this multicenter nonrandomized phase 2 single-arm trial, the treatment outcomes of patients with recurrent or second primary HNSCC who satisfied recursive partitioning analysis class 1 and 2 definitions were evaluated. Between July 11, 2018, and August 12, 2021, 62 patients were consented and screened. Data were evaluated between June and December 2023. Sixty- to 66-Gy IMRT in 30 to 33 daily fractions over 6 to 6.5 weeks with nivolumab, 240 mg, intravenously 2 weeks prior and every 2 weeks for 5 cycles during IMRT, then nivolumab, 480 mg, intravenously every 4 weeks for a total nivolumab duration of 52 weeks. The primary end point was PFS. Secondary end points included OS, incidence, and types of toxic effects, including long-term treatment-related toxic effects, patient-reported outcomes, and correlatives of tissue and blood biomarkers. A total of 62 patients were screened, and 51 were evaluable (median [range] age was 62 [56-67] years; 42 [82%] were male; 6 [12%] had p16+ disease; 38 [75%] had salvage surgery; and 36 [71%.] had neck dissection). With a median follow-up of 24.5 months (95% CI, 19.0-25.0), the estimated 1-year PFS was 61.7% (95% CI, 49.2%-77.4%), rejecting the null hypothesis of 1-year PFS rate of less than 43.8% with 1-arm log-rank test P = .002 within a 1-year timeframe. The most common treatment-related grade 3 or higher adverse event (6 [12%]) was lymphopenia with 2 patients (4%) and 1 patient each (2%) exhibiting colitis, diarrhea, myositis, nausea, mucositis, and myasthenia gravis. Functional Assessment of Cancer Therapy-General and Functional Assessment of Cancer Therapy-Head and Neck Questionnaire quality of life scores remained stable and consistent across all time points. A hypothesis-generating trend favoring worsening PFS and OS in patients with an increase in blood PD1+, KI67+, and CD4+ T cells was observed. This multicenter nonrandomized phase 2 trial of IMRT reirradiation therapy and nivolumab suggested a promising improvement in PFS over historical controls. The treatment was well tolerated and deserves further evaluation. ClinicalTrials.gov Identifier: NCT03521570.

Risk Factors for Prolonged Air Leakage after Uniportal Anatomical Segmentectomy

Abstract Background As the population ages and comorbidities increase, minimal invasive anatomical segmentectomies are increasingly being performed. Complications are to be considered in this vulnerable population. Aims Our aim was to investigate the incidence and risk factors for prolonged air leak in patients undergoing minimally invasive single-port pulmonary segmentectomy at our institution. Methods Retrospective analysis of all patients undergoing uniportal segmentectomy in our department from March 2015 to August 2023. Prolonged air leak (PAL) was defined as an air leak that lasted longer than 5 days. Results 575 segmentectomies were performed using uniportal video-assisted thoracoscopic surgery (uVATS). 374 complex segmentectomies (65.0%) and 405 (70.4%) single segmentectomies were performed. Prolonged air leak occurred in 88 patients (15.3%). Length of stay and duration of drainage were 8.6 (±4.86) and 10.6 (±8.12) days in the PAL subgroup, whereas they wer 3.6 (±2.25) and 2.0 (±1.3) days in the non-PAL group, both statistically significant. Patients with COPD GOLD II/III were significantly more common in the PAL group. Using machine learning, two models were developed that predicted the occurrence of PAL with an accuracy of 70 %. The first model detected the following parameters as significant: removal of segment 2 or 8, diabetes, inhalers, squamous cell carcinoma. The second model recognized DLCO (%), pack-years, FEV1 (%) and operation time as parameters. Furthermore, we used recursive partitioning to develop a decision tree that stratified PAL risk by answering 1-3 yes/no questions. Patients with &amp;gt;28 pack-years and DLCO &amp;lt; 71% had a 77% probability of developing prolonged air leak. Conclusion Severe COPD, low DLCO and FEV1, increased pack-years, inhalers, diabetes, and segment 2 or 8 surgery were identified as risk factors for prolonged air leak. These findings may help in the planning of surgery and the use of sealants at the end of surgery in high-risk patients.

Prognostic Factors and Impact of Therapeutic Intervention in Patients With Brain Metastases at the Initial Presentation.

Background Studies investigating the normative characteristics and prognosis of patients diagnosed with brain metastases (BMs) at the onset of cancer are scarce. Therefore, we analyzed real-world treatment options. Methodology This retrospective study enrolled 112 patients newly diagnosed with BM between May 2006 and October 2021. The variables examined included patients' age, sex, recurrence split analysis, Glasgow prognostic score (GPS), number of lesions, tumor size, peripheral brain tumor edema, targeted therapy, supportive care, chemotherapy, and date of onset. Prognostic factors were assessed using recursive partitioning analysis (RPA), graded prognostic assessment (GPA) scores, and GPS scoring, with magnetic resonance imaging (MRI) andcomputed tomography (CT) studies. Primary treatment comprised whole-brain radiotherapy (WBRT), with regular follow-up. Results Data from 112 survivors were analyzed, revealing a median overall survival time (MST) of 7.7 months, with some patients surviving beyond 24 months post-WBRT. Univariate analysis revealed associations between MST and RPA class, GPS, and treatment modalities (including targeted therapy and chemotherapy). RPA class 2, GPSof 0, and targeted therapy were identified as predictors of better prognosis in the multivariate analysis. In the subgroup not receiving chemotherapy, no significant difference in prognosis was seen between groups with or without WBRT. Conclusions Alongside RPA, scores indicating chronic inflammatory changes, including GPS, were confirmed as crucial prognostic factors. Moreover, treatment with molecularly targeted drugs correlated with favorable prognoses. The treatment-naïve group exhibited poorer prognoses, and WBRT was not deemed a significant prognostic factor in the chemotherapy group.

The [18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study.

We aimed to investigate whether [18F]F-FDG-PET/CT-derived radiomics can classify histologic subtypes and determine the anatomical origin of various malignancies. In this IRB-approved retrospective study, 391 patients (age = 66.7 ± 11.2) with pulmonary (n = 142), gastroesophageal (n = 128) and head and neck (n = 121) malignancies were included. Image segmentation and feature extraction were performed semi-automatically. Two models (all possible subset regression [APS] and recursive partitioning) were employed to predict histology (squamous cell carcinoma [SCC; n = 219] vs. adenocarcinoma [AC; n = 172]), the anatomical origin, and histology plus anatomical origin. The recursive partitioning algorithm outperformed APS to determine histology (sensitivity 0.90 vs. 0.73; specificity 0.77 vs. 0.65). The recursive partitioning algorithm also revealed good predictive ability regarding anatomical origin. Particularly, pulmonary malignancies were identified with high accuracy (sensitivity 0.93; specificity 0.98). Finally, a model for the synchronous prediction of histology and anatomical disease origin resulted in high accuracy in determining gastroesophageal AC (sensitivity 0.88; specificity 0.92), pulmonary AC (sensitivity 0.89; specificity 0.88) and head and neck SCC (sensitivity 0.91; specificity 0.92). Adding PET-features was associated with marginal incremental value for both the prediction of histology and origin in the APS model. Overall, our study demonstrated a good predictive ability to determine patients' histology and anatomical origin using [18F]F-FDG-PET/CT-derived radiomics features, mainly from CT.

Residency program factors affecting ACVS board examination pass rates from 2017 to 2021: A survey of ACVS small animal surgery residency programs.

To identify small animal surgery (SAS) residency program factors associated with board examination pass rates for the American College of Veterinary Surgeons (ACVS) board certification examinations. Online survey. Program directors of ACVS-registered SAS residency programs. An online survey was distributed to eligible 2021 ACVS SAS residency program directors. Respondents provided program information and Phase I and Phase II board examination pass rate data from 2017 to 2021. Programs were grouped based on overall combined board examination pass rates as "excellent" (≥90%) versus "adequate" (<90%). Data were analyzed using recursive partitioning. Responses from 36 ACVS SAS residency programs (18 private practice [PP], 18 academic programs [AP]) were included. Pass rates were considered "excellent" for 17/36 programs (11/17 AP, 6/17 PP). The strongest indicator of "excellent" programs was most mentors participating in rounds >75% of the time. Of those, the second indicator of "excellent" programs was a soft tissue caseload of <7 surgeries/resident/week. If the soft tissue caseload was >7 surgeries/resident/week, a high orthopedic caseload (>9 surgeries/resident/week) was indicative of "excellent" programs. If most mentors were not participating in rounds >75% of the time, APs were more likely to be "excellent." SAS APs may have higher ACVS board examination pass rates. Mentor involvement, caseload, and residency practice type may influence board examination pass rates. ACVS SAS residency programs' board examination pass rates may improve by optimizing mentor involvement and caseload regardless of practice type.

Squamous metaplasia is an indicator of acute exacerbation in patients with usual interstitial pneumonia / idiopathic pulmonary fibrosis

BackgroundAcute exacerbation (AE) is a potentially lethal event in patients with usual interstitial pneumonia/idiopathic pulmonary fibrosis (UIP/IPF). However, to date, no pathological predictors of AE have been identified. This retrospective study aimed to elucidate the pathological features that could predict AE in patients with UIP. MethodsWe reviewed the pathological findings of 91 patients with UIP/IPF and correlated these findings with AE events. Thirteen histological variables related to acute lung injury were evaluated by three independent observers and classified as positive or negative. The patients’ clinical data during follow-up were collected and reviewed for AE. A recursive partition using the Gini index for the prediction of AE was performed, with each pathological finding as a candidate for branching. ResultsTwenty patients (22%) developed AE during the median follow-up duration of 40 months. Thirty-eight patients died (15 due to AE and 23 for other reasons). The median time interval from surgical lung biopsy to AE onset was 497 (interquartile range: 901–1657) days. Histologically, squamous metaplasia was positively associated with AE (odds ratio: 4.7, P = 0.015) and worse event-free survival in patients with UIP (P = 0.04). Leaf scoring based on the Gini index for recursive partition, including five positive findings (squamous metaplasia, neutrophilic infiltration, septal widening, Kuhn's hyaline, and fibrin), showed a sensitivity of 90% with a specificity of 74.7% (area under curve: 0.89). ConclusionsWe found that squamous metaplasia is an important histopathological finding that predicts AE events and tends to unfavorable outcome in patients with UIP/IPF.