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340 Background: TIS, an anti-programmed cell death protein 1 (PD-1) antibody, + chemo as 1L therapy demonstrated statistically significant and clinically meaningful improvement in overall survival (OS) vs PBO + chemo in patients with advanced or metastatic ESCC (hazard ratio [HR] 0.66 [95% confidence interval (CI) 0.54, 0.80]; P< 0.0001), with a manageable safety profile, at interim analysis of the phase 3, double-blind RATIONALE-306 study (NCT03783442). Here, we report data from the non-Asia subgroup (Europe, Northern America, and Oceania). Methods: Adults with advanced or metastatic ESCC, with no prior systemic treatment for advanced disease were randomized 1:1, (stratified by region, prior definitive therapy, and investigator [INV]-chosen chemo) to receive TIS 200 mg intravenously (IV) once every 3 weeks (Q3W) (Arm A) or PBO IV Q3W (Arm B), with platinum + fluoropyrimidine, or platinum + paclitaxel until disease progression by INV per RECIST v1.1, intolerable toxicity, or withdrawal. The primary endpoint was OS in the intent-to-treat population. Secondary endpoints included: progression-free survival (PFS), objective response rate (ORR), and duration of response (DoR) by INV per RECIST v1.1; OS in the programmed death-ligand 1 score ≥10%; and safety. Results: Of 649 randomized patients, 163 (25.1%) were from the non-Asia subgroup (Arm A, n = 83; Arm B, n = 80). At data cutoff (Feb 28, 2022), the median study follow-up time in the non-Asia subgroup was 16.0 months (mo) in Arm A vs 8.4 mo in Arm B. OS (median 16.3 vs 9.0 mo; unstratified HR 0.66 [95% CI 0.45, 0.96]) and PFS (median 7.7 vs 5.5 mo; unstratified HR 0.59 [95% CI 0.41, 0.83]) were improved in Arm A vs Arm B, respectively. Arm A had higher ORR (61.4% vs 41.3%, odds ratio 2.27 [95% CI 1.21, 4.25]) and longer median DoR (7.1 mo [95% CI 5.6, 9.6] vs 5.7 mo [95% CI 3.8, 8.3]) than Arm B. More patients in Arm A vs Arm B experienced ≥1 treatment-related adverse event (TRAE; 94.0% vs 88.5%), serious TRAEs (25.3% vs 17.9%), and discontinuation due to treatment-emergent AEs (42.2% vs 35.9%, respectively). Similar proportions of patients in Arm A vs Arm B had ≥grade 3 TRAEs (56.6% vs 52.6%), and TRAEs leading to death (1.2% vs 1.3%), respectively. Conclusions: In the non-Asia subgroup, 1L TIS + chemo showed a clinically meaningful improvement in OS vs PBO + chemo in patients with advanced or metastatic ESCC, with a manageable safety profile, consistent with published results in the overall population. Clinical trial information: NCT03783442 .

Vitamin C remains an important, yet frequently unassessed, component of a healthy immune system though it may prove useful in alleviating the chronic inflammatory processes underlying chronic diseases such as coronary artery disease (CAD). Recent research identified a sizeable proportion of the United States population with insufficient vitamin C plasma levels and significant associations to both acute and chronic inflammation. This cross-sectional study used the 2003–2006 NHANES surveys data to extrapolate associations between plasma vitamin C levels (deficiency, hypovitaminosis, inadequate, adequate, and saturating) and CAD through inflammation (C-reactive protein and red cell distribution width). Increased reports of CAD diagnosis were identified in participants with vitamin C deficiency (OR: 2.31, CI: 1.49–3.58) and inadequate plasma levels (OR: 1.39, CI: 1.03–1.87). No significant correlation was identified between any other plasma vitamin C quintiles and CAD. When inflammation was controlled, previous associations in the deficient level of plasma vitamin C were no longer significant in association with CAD and participants with inadequate plasma vitamin C showed a reduced association to CAD diagnoses (OR: 0.33, CI: 0.13-0.86). Most chronic inflammation and vitamin C plasma statuses do not demonstrate specific signs or symptoms until the deficient level of vitamin C and/or disease. Thus, increased surveillance of both, and healthy nutritional habits remain crucial modifiable risk factors for disease prevention.

Chronic kidney disease (CKD) progression involves morphological changes in the kidney, such as decreased length and thickness, with associated histopathological alterations. However, the relationship between morphological changes in the kidneys and glomerular filtration rate (GFR) has not been quantitatively and comprehensively evaluated. We evaluated the three-dimensional size and shape of the kidney using computed tomography (CT)-derived features in relation to kidney function. We included 257 patients aged ≥18 years who underwent non-contrast abdominal CT at the Inha University Hospital. The features were quantified using predefined algorithms in the pyRadiomics package after kidney segmentation. All features, except for flatness, significantly correlated with estimated GFR (eGFR). The surface-area-to-volume ratio (SVR) showed the strongest negative correlation (r = −0.75, p < 0.0001). Kidney size features, such as volume and diameter, showed moderate to high positive correlations; other morphological features showed low to moderate correlations. The calculated area under the receiver operating characteristic (ROC) curve (AUC) for different features ranged from 0.51 (for elongation) to 0.86 (for SVR) for different eGFR thresholds. Diabetes patients had weaker correlations between the studied features and eGFR and showed less bumpy surfaces in three-dimensional visualization. We identified alterations in the CKD kidney based on various three-dimensional shape and size features, with their potential diagnostic value.

Sonic hedgehog signaling regulates processes of embryonic development across multiple tissues, yet factors regulating context-specific Shh signaling remain poorly understood. Exome sequencing of families with polymicrogyria (disordered cortical folding) revealed multiple individuals with biallelic deleterious variants in TMEM161B , which encodes a multi-pass transmembrane protein of unknown function. Tmem161b null mice demonstrated holoprosencephaly, craniofacial midline defects, eye defects, and spinal cord patterning changes consistent with impaired Shh signaling, but were without limb defects, suggesting a CNS-specific role of Tmem161b. Tmem161b depletion impaired the response to Smoothened activation in vitro and disrupted cortical histogenesis in vivo in both mouse and ferret models, including leading to abnormal gyration in the ferret model. Tmem161b localizes non-exclusively to the primary cilium, and scanning electron microscopy revealed shortened, dysmorphic, and ballooned ventricular zone cilia in the Tmem161b null mouse, suggesting that the Shh-related phenotypes may reflect ciliary dysfunction. Our data identify TMEM161B as a regulator of cerebral cortical gyration, as involved in primary ciliary structure, as a regulator of Shh signaling, and further implicate Shh signaling in human gyral development.

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Results: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73–0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Conclusion: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Advances in knowledge: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

ImportanceCytokine storm due to COVID-19 can cause high morbidity and mortality and may be more common in patients with cancer treated with immunotherapy (IO) due to immune system activation.ObjectiveTo determine the association of baseline immunosuppression and/or IO-based therapies with COVID-19 severity and cytokine storm in patients with cancer.Design, Setting, and ParticipantsThis registry-based retrospective cohort study included 12 046 patients reported to the COVID-19 and Cancer Consortium (CCC19) registry from March 2020 to May 2022. The CCC19 registry is a centralized international multi-institutional registry of patients with COVID-19 with a current or past diagnosis of cancer. Records analyzed included patients with active or previous cancer who had a laboratory-confirmed infection with SARS-CoV-2 by polymerase chain reaction and/or serologic findings.ExposuresImmunosuppression due to therapy; systemic anticancer therapy (IO or non-IO).Main Outcomes and MeasuresThe primary outcome was a 5-level ordinal scale of COVID-19 severity: no complications; hospitalized without requiring oxygen; hospitalized and required oxygen; intensive care unit admission and/or mechanical ventilation; death. The secondary outcome was the occurrence of cytokine storm.ResultsThe median age of the entire cohort was 65 years (interquartile range [IQR], 54-74) years and 6359 patients were female (52.8%) and 6598 (54.8%) were non-Hispanic White. A total of 599 (5.0%) patients received IO, whereas 4327 (35.9%) received non-IO systemic anticancer therapies, and 7120 (59.1%) did not receive any antineoplastic regimen within 3 months prior to COVID-19 diagnosis. Although no difference in COVID-19 severity and cytokine storm was found in the IO group compared with the untreated group in the total cohort (adjusted odds ratio [aOR], 0.80; 95% CI, 0.56-1.13, and aOR, 0.89; 95% CI, 0.41-1.93, respectively), patients with baseline immunosuppression treated with IO (vs untreated) had worse COVID-19 severity and cytokine storm (aOR, 3.33; 95% CI, 1.38-8.01, and aOR, 4.41; 95% CI, 1.71-11.38, respectively). Patients with immunosuppression receiving non-IO therapies (vs untreated) also had worse COVID-19 severity (aOR, 1.79; 95% CI, 1.36-2.35) and cytokine storm (aOR, 2.32; 95% CI, 1.42-3.79).Conclusions and RelevanceThis cohort study found that in patients with cancer and COVID-19, administration of systemic anticancer therapies, especially IO, in the context of baseline immunosuppression was associated with severe clinical outcomes and the development of cytokine storm.Trial RegistrationClinicalTrials.gov Identifier: NCT04354701

Abstract Purpose: Targeting focal adhesion kinase (FAK) renders checkpoint immunotherapy effective in pancreatic ductal adenocarcinoma (PDAC) mouse model. Defactinib is a highly potent oral FAK inhibitor that has a tolerable safety profile. Patients and Methods: We conducted a multicenter, open-label, phase I study with dose escalation and expansion phases. In dose escalation, patients with refractory solid tumors were treated at five escalating dose levels of defactinib and gemcitabine to identify a recommended phase II dose (RP2D). In expansion phase, patients with metastatic PDAC who progressed on frontline treatment (refractory cohort) or had stable disease (SD) after at least 4 months of standard gemcitabine/nab-paclitaxel (maintenance cohort) were treated at RP2D. Pre- and posttreatment tumor biopsies were performed to evaluate tumor immunity. Results: The triple drug combination was well-tolerated, with no dose-limiting toxicities. Among 20 treated patients with refractory PDAC, the disease control rate (DCR) was 80%, with one partial response (PR) and 15 SDs, and the median progression-free survival (PFS) and overall survival (OS) were 3.6 and 7.8 months, respectively. Among 10 evaluable patients in the maintenance cohort, DCR was 70% with one PR and six SDs. Three patients with SD came off study due to treatment- or disease-related complications. The median PFS and OS on study treatment were 5.0 and 8.3 months, respectively. Conclusions: The combination of defactinib, pembrolizumab, and gemcitabine was well-tolerated and safe, had promising preliminary efficacy, and showed biomarker activity in infiltrative T lymphocytes. Efficacy of this strategy may require incorporation of more potent chemotherapy in future studies.

AbstractPurposeMetallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images.MethodsThe proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours.ResultsFor in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm3for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm3, respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm3for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt–chromium alloys by different vendors.ConclusionsA component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof‐of‐concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head‐and‐neck patients where tight margins are required to spare organs at risk.

Abstract Chest computed tomography (CT) is one of the most frequently performed imaging studies. Incidental osseous and articular findings are commonly encountered in chest CTs in daily practice. The spectrum of findings is broad, varying from benign to malignant, and interpretation of these incidental musculoskeletal findings could be challenging for radiologists. In this review, we provide a systematic algorithmic approach for the diagnosis of common articular findings seen on chest CT with recommendations for appropriate follow-up evaluation.