Comparing visualization performance of liquid embolic agents using a novel injectable phantom.

BACKGROUND: Radiographic visualization of liquid embolic agents (LEAs) during embolization procedures in neurovascular territory represents a crucial feature to ensure efficacy and safety for the patients during endovascular treatment of arteriovenous shunting lesions. Radiopacity of available LEAs varies significantly and limited methods are currently available for comparison. The purpose of this study was to compare the contrast resolution (CR) during injection under blank roadmap of various LEAs, as well as standard contrast material. METHODS: An injectable angiographic phantom was designed consisting of parallel tubings between 313 and 1000 micron. Under roadmap, eight radiopaque liquid agents were injected and analyzed: Onyx18®, 34® Squid®12, 18, PHIL®25% (PHIL®25), PHIL®30% (PHIL®30).TrufillTM (NBCA), 30% dilution and Omnipaque®300. CR was evaluated as a contrast to noise ratio (CNR) and calculated as mean peak signal (Sa) minus mean background signal (Sb) divided by the standard deviation of the background signal (Std) (CNR=Sa-SbStd). RESULTS: Omnipaque 300 and NBCA were found to have the highest CR. PHIL®25 demonstrated the lowest CNR (45% of Omnipaque CNR). Onyx 18 and 34 (Both around 82% of Omnipaque CNR) demonstrated higher CNR compared to Squid®12 and 18 (52-55% of Omnipaque CNR). On average, at 500 micron there is a >70% reduction in CNR, and at 313 micron there is a 90% reduction in CNR compared to 1000 micron. CONCLUSIONS: Significantly different CNR between most LEAs and iodinated contrast media was evident under roadmap conditions and should be considered prior to injection.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.

OBJECTIVE: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm. MATERIALS AND METHODS: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE. RESULTS: A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24). CONCLUSION: Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

A case series of osseous metastases in patients with glioblastoma.

BACKGROUND: Extracranial metastases occur in <2% of cases of glioblastoma (GBM). When metastases do occur, bone is the most common destination. Herein, we review clinical characteristics of GBM patients with osseous metastases and evaluate both potential risk factors and prognostic significance. METHODS: Using an institutional database, we identified and retrospectively analyzed 6 patients with both GBM and osseous metastases. We collected data on patient demographics, tumor genetics, clinical courses, and outcomes. Given the rarity of metastatic GBM, we conducted historical comparisons using previously published literature. RESULTS: Five patients with osseous metastases (83%) were male, with a median age of 46 years at GBM diagnosis (range: 20-84). All patients had IDH-wildtype, MGMT promoter unmethylated GBM and 5 (83%) had alterations in TP53. All patients underwent surgical resection for GBM followed by radiation with concurrent and adjuvant temozolomide. Four patients (67%) received bevacizumab prior to bone metastasis diagnosis. Bone metastases were discovered at a median of 12.2 months (range: 5.3-35.2) after GBM diagnosis and 4.8 months after starting bevacizumab (range: 3.5-13.2). Three patients (50%) received immunotherapy. After osseous metastasis diagnosis, the median survival was 25 days (range: 13-225). CONCLUSION: In our cohort, most patients were male and young at the time of GBM diagnosis. All patients had IDH-wildtype, MGMT promoter unmethylated GBM, and most had alterations in TP53, which may be important for osseous metastasis. Most patients received bevacizumab, which has been associated with earlier metastasis. Osseous metastases of GBM occur and portend a dismal prognosis in an already aggressive malignancy.

Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma.

Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.

Hyperkinetic Biliary Dyskinesia: An Underrecognized Problem With Good Surgical Outcomes After Cholecystectomy.

Introduction While surgical indications for symptomatic cholelithiasis and biliary hypokinesia are clear, hyperkinetic biliary dyskinesia (HBD) is an underrecognized condition with poorly defined symptomology and management guidelines. HBD is typically defined as a gallbladder ejection fraction (EF) ≥ 80% on a hepatobiliary iminodiacetic acid (HIDA) scan. We aimed to identify the prevalence and radiographic reporting of HBD, physician referral patterns, and clinical outcomes following cholecystectomy. Methods  A retrospective cohort study of patients with HIDA scans completed over 21 years at our tertiary care hospital was performed. Demographics, symptomatology, referral patterns, and operative data were collected. HBD was defined as HIDA EF ≥80%. Patients with HBD who underwent cholecystectomy were analyzed. ANOVA and chi-square tests were used to compare variables among patients with or without symptom improvement using Statistical Product and Service Solutions (SPSS; IBM SPSS Statistics for Windows, Armonk, NY). Results Of 1,997 patients (73% female, mean age 51.7 years) who had HIDA scans with reported EF, 730 (36.6%) had an EF≥80%. Only 13.7% of HIDA scans with EF≥80% were reported as hyperkinetic, and the rest are "normal". Cholecystectomy was performed in 57 (7.8%) patients with EF≥80%, most being elective (89.5%) and all minimally invasive. Primary care physicians (PCPs) referred most elective cases to surgery (61.4%). The median time from HIDA to cholecystectomy was 146 days. Chronic cholecystitis was common in pathology (82.5%), while 38.6% had cholelithiasis. Overall, 53 patients (93.0%) reported symptom improvement at a median follow-up of 17.0 days. Patients without improvement had a higher prevalence of chronic gastrointestinal conditions (p<0.05), but not significantly more cholelithiasis, cholecystitis, time to surgery, or elective surgery status. Conclusions HBD is common but often underdiagnosed and thus likely underrecognized by treating physicians. Most HBD patients benefit from cholecystectomy, regardless of cholelithiasis. Patients with persistent symptoms after cholecystectomy may have confounding gastrointestinal diagnoses. Increased awareness among radiologists, referring PCPs, gastroenterologists, and surgeons about HBD and postoperative outcomes is needed to ensure that HBD is adequately treated.

Texture tomography, a versatile framework to study crystalline texture in 3D.

Crystallographic texture is a key organization feature of many technical and biological materials. In these materials, especially hierarchically structured ones, the preferential alignment of the nano constituents heavily influences the macroscopic behavior of the material. To study local crystallographic texture with both high spatial and angular resolution, we developed Texture Tomography (TexTOM). This approach allows the user to model the diffraction data of polycrystalline materials using the full reciprocal space of the crystal ensemble and describe the texture in each voxel via an orientation distribution function, hence it provides 3D reconstructions of the local texture by measuring the probabilities of all crystal orientations. The TexTOM approach addresses limitations associated with existing models: it correlates the intensities from several Bragg reflections, thus reducing ambiguities resulting from symmetry. Further, it yields quantitative probability distributions of local real space crystal orientations without further assumptions about the sample structure. Finally, its efficient mathematical formulation enables reconstructions faster than the time scale of the experiment. This manuscript presents the mathematical model, the inversion strategy and its current experimental implementation. We show characterizations of simulated data as well as experimental data obtained from a synthetic, inorganic model sample: the silica-witherite biomorph. TexTOM provides a versatile framework to reconstruct 3D quantitative texture information for polycrystalline samples; it opens the door for unprecedented insights into the nanostructural makeup of natural and technical materials.

Variations in germ cell tumor histology by age and implications for cancer-specific survival among pediatric and adult males: A population-based study.

PURPOSE: Few studies have quantified differences in histology and implications for survival between male children and adults with germ cell tumors (GCT). We evaluated these differences and associations with cancer-specific survival (CSS) using Surveillance, Epidemiology, and End Results (SEER) cancer registries. METHODS: SEER (1988-2016) was used to identify male patients 0 to 40 years of age diagnosed with seminoma and nonseminomatous GCT (NSGCT). Demographic and tumor characteristics were tabulated with histology distributions compared by age group (0-4, 12-18, 19-40 years old). CSS was evaluated in multivariable Cox proportional hazards regression models. RESULTS: Among 27,204 patients identified, 1,538 (5.7%) were pediatric (0-18 years). Seminoma (54.3%) predominated in adult patients (ages 19-40). Among 0 to 4 years-old, yolk sac tumor (71.2%) and teratoma (21.5%) were most common. Mixed GCT (52.7%) was most prevalent among 12 to 18 years-old with seminoma, embryonal, and teratoma occurring in 12 to 15% each. Relative to pediatric patients, adult patients had similar CSS for seminoma but worse CSS for NSGCT on Kaplan-Meier curves with 9 years mean follow-up. Choriocarcinoma and yolk sac tumors carried the worst prognosis relative to seminoma for both children (HR 5.7 and HR 11.1, respectively, both P < 0.01) and adults (HR 4.6 and HR 4.6, respectively, both P < 0.01) adjusted for stage. CONCLUSION: Histology of GCTs vary by age with yolk sac tumors and teratoma predominating for male patients 0 to 4 years, mixed GCT for 12 to 18 years, and seminoma for 19 to 40 years. Pediatric patients with NSGCT had higher CSS than their adult counterparts. Mixed GCT represented an increasing proportion of GCT over the study period. Age, stage, and histology impact CSS in both pediatric and adult populations.

Expression of tumor antigens within an oncolytic virus enhances the anti-tumor T cell response.

Although patients benefit from immune checkpoint inhibition (ICI) therapy in a broad variety of tumors, resistance may arise from immune suppressive tumor microenvironments (TME), which is particularly true of hepatocellular carcinoma (HCC). Since oncolytic viruses (OV) can generate a highly immune-infiltrated, inflammatory TME, OVs could potentially restore ICI responsiveness via recruitment, priming, and activation of anti-tumor T cells. Here we find that on the contrary, an oncolytic vesicular stomatitis virus, expressing interferon-ß (VSV-IFNß), antagonizes the effect of anti-PD-L1 therapy in a partially anti-PD-L1-responsive model of HCC. Cytometry by Time of Flight shows that VSV-IFNß expands dominant anti-viral effector CD8 T cells with concomitant relative disappearance of anti-tumor T cell populations, which are the target of anti-PD-L1. However, by expressing a range of HCC tumor antigens within VSV, combination OV and anti-PD-L1 therapeutic benefit could be restored. Our data provide a cautionary message for the use of highly immunogenic viruses as tumor-specific immune-therapeutics by showing that dominant anti-viral T cell responses can inhibit sub-dominant anti-tumor T cell responses. However, through encoding tumor antigens within the virus, oncolytic virotherapy can generate anti-tumor T cell populations upon which immune checkpoint blockade can effectively work.

Comparison of Artificial Intelligence to Resident Performance on Upper-Extremity Orthopaedic In-Training Examination Questions.

Purpose: Currently, there is a paucity of prior investigations and studies examining applications for artificial intelligence (AI) in upper-extremity (UE) surgical education. The purpose of this investigation was to assess the performance of a novel AI tool (ChatGPT) on UE questions on the Orthopaedic In-Training Examination (OITE). We aimed to compare the performance of ChatGPT to the examination performance of hand surgery residents. Methods: We selected questions from the 2020-2022 OITEs that focused on both the hand and UE as well as the shoulder and elbow content domains. These questions were divided into two categories: those with text-only prompts (text-only questions) and those that included supplementary images or videos (media questions). Two authors (B.K.F. and G.S.M.) converted the accompanying media into text-based descriptions. Included questions were inputted into ChatGPT (version 3.5) to generate responses. Each OITE question was entered into ChatGPT three times: (1) open-ended response, which requested a free-text response; (2) multiple-choice responses without asking for justification; and (3) multiple-choice response with justification. We referred to the OITE scoring guide for each year in order to compare the percentage of correct AI responses to correct resident responses. Results: A total of 102 UE OITE questions were included; 59 were text-only questions, and 43 were media-based. ChatGPT correctly answered 46 (45%) of 102 questions using the Multiple Choice No Justification prompt requirement (42% for text-based and 44% for media questions). Compared to ChatGPT, postgraduate year 1 orthopaedic residents achieved an average score of 51% correct. Postgraduate year 5 residents answered 76% of the same questions correctly. Conclusions: ChatGPT answered fewer UE OITE questions correctly compared to hand surgery residents of all training levels. Clinical relevance: Further development of novel AI tools may be necessary if this technology is going to have a role in UE education.

3-Position Biaryl Endochin-like Quinolones with Enhanced Antimalarial Performance.

ELQ-300 is a potent antimalarial drug with activity against blood, liver, and vector stages of the disease. A prodrug, ELQ-331, exhibits reduced crystallinity and improved in vivo efficacy in preclinical testing, and currently, it is in the developmental pipeline for once-a-week dosing for oral prophylaxis against malaria. Because of the high cost of developing a new drug for human use and the high risk of drug failure, it is prudent to have a back-up plan in place. Here we describe ELQ-596, a member of a new subseries of 3-biaryl-ELQs, with enhanced potency in vitro against multidrug-resistant Plasmodium falciparum parasites. ELQ-598, a prodrug of ELQ-596 with diminished crystallinity, is more effective vs murine malaria than its progenitor ELQ-331 by 4- to 10-fold, suggesting that correspondingly lower doses could be used to protect and cure humans of malaria. With a longer bloodstream half-life in mice compared to its progenitor, ELQ-596 highlights a novel series of next-generation ELQs with the potential for once-monthly dosing for protection against malaria infection. Advances in the preparation of 3-biaryl-ELQs are presented along with preliminary results from experiments to explore key structure-activity relationships for drug potency, selectivity, pharmacokinetics, and safety.

Spontaneous and double-strand break repair-associated quasipalindrome and frameshift mutagenesis in budding yeast: role of mismatch repair.

Although gene conversion (GC) in Saccharomyces cerevisiae is the most error-free way to repair double-strand breaks (DSBs), the mutation rate during homologous recombination is 1,000 times greater than during replication. Many mutations involve dissociating a partially copied strand from its repair template and re-aligning with the same or another template, leading to -1 frameshifts in homonucleotide runs, quasipalindrome (QP)-associated mutations and microhomology-mediated interchromosomal template switches. We studied GC induced by HO endonuclease cleavage at MATα, repaired by an HMR::KI-URA3 donor. We inserted into HMR::KI-URA3 an 18-bp inverted repeat where one arm had a 4-bp insertion. Most GCs yield MAT::KI-ura3::QP + 4 (Ura-) outcomes, but template-switching produces Ura+ colonies, losing the 4-bp insertion. If the QP arm without the insertion is first encountered by repair DNA polymerase and is then (mis)used as a template, the palindrome is perfected. When the QP + 4 arm is encountered first, Ura+ derivatives only occur after second-end capture and second-strand synthesis. QP + 4 mutations are suppressed by mismatch repair (MMR) proteins Msh2, Msh3, and Mlh1, but not Msh6. Deleting Rdh54 significantly reduces QP mutations only when events creating Ura+ occur in the context of a D-loop but not during second-strand synthesis. A similar bias is found with a proofreading-defective DNA polymerase mutation (poI3-01). DSB-induced mutations differed in several genetic requirements from spontaneous events. We also created a + 1 frameshift in the donor, expanding a run of 4 Cs to 5 Cs. Again, Ura+ recombinants markedly increased by disabling MMR, suggesting that MMR acts during GC but favors the unbroken, template strand.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Immune Checkpoint Inhibitors and Glioblastoma: A Review on Current State and Future Directions.

Glioblastoma (GBM) is the most prevalent malignant tumor of the central nervous system. The prognosis of GBM is grim, with a median overall survival of 14.6 months and only 6.9% of patients surviving 5 years after the initial diagnosis. Despite poor outcomes, standard therapy of surgical resection, radiotherapy, chemotherapy, and tumor-treating fields has remained largely unchanged. The introduction of immune checkpoint inhibitors (ICI) has been a paradigm shift in oncology, with efficacy across a broad spectrum of cancer types. Nonetheless, investigations of ICIs in both newly diagnosed and recurrent GBM have thus far been disappointing. This lack of clinical benefit has been largely attributed to the highly immunosuppressive nature of GBM. However, immunotherapy still holds promise for the treatment of GBM, with combinatorial strategies offering hope for potentially overcoming these current limitations. In this review, we discuss the outcomes of clinical trials employing ICIs in patients with GBM. Afterward, we review ICI combination strategies and how these combinations may overcome the immunosuppressive microenvironment of GBM in the context of preclinical/clinical evidence and ongoing clinical trials.

Ghost roads and the destruction of Asia-Pacific tropical forests.

Roads are expanding at the fastest pace in human history. This is the case especially in biodiversity-rich tropical nations, where roads can result in forest loss and fragmentation, wildfires, illicit land invasions and negative societal effects1-5. Many roads are being constructed illegally or informally and do not appear on any existing road map6-10; the toll of such 'ghost roads' on ecosystems is poorly understood. Here we use around 7,000 h of effort by trained volunteers to map ghost roads across the tropical Asia-Pacific region, sampling 1.42 million plots, each 1 km2 in area. Our intensive sampling revealed a total of 1.37 million km of roads in our plots-from 3.0 to 6.6 times more roads than were found in leading datasets of roads globally. Across our study area, road building almost always preceded local forest loss, and road density was by far the strongest correlate11 of deforestation out of 38 potential biophysical and socioeconomic covariates. The relationship between road density and forest loss was nonlinear, with deforestation peaking soon after roads penetrate a landscape and then declining as roads multiply and remaining accessible forests largely disappear. Notably, after controlling for lower road density inside protected areas, we found that protected areas had only modest additional effects on preventing forest loss, implying that their most vital conservation function is limiting roads and road-related environmental disruption. Collectively, our findings suggest that burgeoning, poorly studied ghost roads are among the gravest of all direct threats to tropical forests.

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.

OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. RESULTS: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). CONCLUSION: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.

Cerebrospinal Fluid Liquid Biopsies in the Evaluation of Adult Gliomas.

PURPOSE OF REVIEW: This review aims to discuss recent research regarding the biomolecules explored in liquid biopsies and their potential clinical uses for adult-type diffuse gliomas. RECENT FINDINGS: Evaluation of tumor biomolecules via cerebrospinal fluid (CSF) is an emerging technology in neuro-oncology. Studies to date have already identified various circulating tumor DNA, extracellular vesicle, micro-messenger RNA and protein biomarkers of interest. These biomarkers show potential to assist in multiple avenues of central nervous system (CNS) tumor evaluation, including tumor differentiation and diagnosis, treatment selection, response assessment, detection of tumor progression, and prognosis. In addition, CSF liquid biopsies have the potential to better characterize tumor heterogeneity compared to conventional tissue collection and CNS imaging. Current imaging modalities are not sufficient to establish a definitive glioma diagnosis and repeated tissue sampling via conventional biopsy is risky, therefore, there is a great need to improve non-invasive and minimally invasive sampling methods. CSF liquid biopsies represent a promising, minimally invasive adjunct to current approaches which can provide diagnostic and prognostic information as well as aid in response assessment.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). RESULTS: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). CONCLUSION: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

Exploring Trends in Immediate Postresidency Career Paths: A Multi-year Analysis of Plastic Surgery Resident Graduates Across the United States.

Background: Plastic surgery is one of the most diverse specialties in medicine. Because of the competitiveness of plastic surgery residency, applicants are entering the field with increased experience and more developed interests in specific specialties. Programs and prospective applicants may find it beneficial to know trends in the career paths of recent graduates. Objectives: To identify trends in postresidency career paths for plastic surgery graduates. Methods: Data from all integrated plastic surgery residency programs were analyzed from 2013 to 2022. Eighty-eight residency programs were analyzed for review. Residency websites were the primary source of data. Postresidency career paths were categorized into subspecialty fellowships, academic practice, or private practice. Secondary data included program rank, size of the program, associated fellowship program, associated independent program, and program location. Results: Seventy-three programs met the inclusion criteria. Private practice was the most common immediate postgraduation path. Microvascular and aesthetic fellowships demonstrated maximum growth in the last 10 years, followed by hand fellowships. Programs ranked in the top 25 by Doximity reputation were significantly associated with graduates going into craniofacial (P = .05) and microvascular fellowship (P = .021), and immediate academic practice (P = .011). Lower-ranked programs were correlated with higher levels of graduates entering directly into private/community hospital practice (ρ = 0.327). Conclusions: Life after residency is a necessary consideration for training physicians. Understanding trends in postresidency career paths could help programs and prospective applicants make more informed decisions on what programs may offer the best opportunities to pursue their desired career path.

Global dominance of lianas over trees is driven by forest disturbance, climate and topography.

Growing evidence suggests that liana competition with trees is threatening the global carbon sink by slowing the recovery of forests following disturbance. A recent theory based on local and regional evidence further proposes that the competitive success of lianas over trees is driven by interactions between forest disturbance and climate. We present the first global assessment of liana-tree relative performance in response to forest disturbance and climate drivers. Using an unprecedented dataset, we analysed 651 vegetation samples representing 26,538 lianas and 82,802 trees from 556 unique locations worldwide, derived from 83 publications. Results show that lianas perform better relative to trees (increasing liana-to-tree ratio) when forests are disturbed, under warmer temperatures and lower precipitation and towards the tropical lowlands. We also found that lianas can be a critical factor hindering forest recovery in disturbed forests experiencing liana-favourable climates, as chronosequence data show that high competitive success of lianas over trees can persist for decades following disturbances, especially when the annual mean temperature exceeds 27.8°C, precipitation is less than 1614 mm and climatic water deficit is more than 829 mm. These findings reveal that degraded tropical forests with environmental conditions favouring lianas are disproportionately more vulnerable to liana dominance and thus can potentially stall succession, with important implications for the global carbon sink, and hence should be the highest priority to consider for restoration management.

Des preuves de plus en plus nombreuses suggèrent que la competition entre lianes et les arbres menace le puits de carbone mondial en ralentissant la récupération des forêts après une perturbation. Une théorie récente, fondée sur des observations locales et régionales, propose en outre que le succès compétitif des lianes sur les arbres est dû aux interactions entre la perturbation forestière et le climat. Nous présentons la première évaluation mondiale de la performance relative des lianes par rapport aux arbres en réponse aux perturbations forestières et aux facteurs climatiques. En utilisant un ensemble de données sans précédent, nous avons analysé 651 échantillons de végétation représentant 26,538 lianes et 82,802 arbres, issus de 556 emplacements uniques dans le monde entier, tirés de 83 publications. Les résultats montrent que les lianes ont de meilleure performances par rapport aux arbres (augmentation du ratio liane-arbre) lorsque les forêts sont perturbées, sous des zones chaudes aves précipitations faibles, et vers les basses altitudes tropicales. Nous avons également constaté que les lianes peuvent être un facteur critique entravant la récupération des forêts dans les forêts perturbées connaissant des climats favorables aux lianes, car les données de chronoséquence montrent que le succès compétitif élevé des lianes sur les arbres peut persister pendant des décennies après les perturbations, surtout lorsque la température annuelle moyenne dépasse 27.8°C, que les précipitations sont inférieures à 1614 mm et que le déficit hydrique climatique est supérieur à 829 mm. Ces découvertes révèlent que les forêts tropicales dégradées avec des conditions environnementales favorables aux lianes sont disproportionnellement plus vulnérables à la dominance des lianes, et peuvent ainsi potentiellement entraver la succession, avec d'importantes implications pour le puits de carbone mondial et devraient donc être la plus haute priorité à considérer pour la gestion de la restauration.