Citations

Guidelines for diagnosing pulmonary embolism (PE) start with a risk assessment using a pretest probability (PTP) tool, followed by D-dimer testing or computed tomography pulmonary angiography (CTPA) depending on risk. The project aimed to develop an electronic clinical quality measure (eCQM) to encourage broader use of a validated PTP scoring tool in emergency departments (EDs) to more accurately diagnose PE and to reduce unnecessary CTPAs. To identify a value set to accurately identify CTPA and abnormal D-dimer tests using standard classification systems and clinical vocabularies (ie, Current Procedural Terminology [CPT], Logical Observation Identifiers Names and Codes [LOINC], systematized nomenclature of medicine clinical terms [SNOMED CT]) across 3 academic United States health care systems. A comprehensive value set to identify CTPAs was selected, which contained 31 codes. Additionally, each health care system had unique, site-specific codes to more granularly identify CTPAs. Three health care systems representing 38 EDs from across the country submitted data from all ED encounters between September 12, 2022, and January 11, 2023. Imaging types were reviewed from each of the CPT codes and LOINC. The project evaluated whether a D-dimer was obtained using CPT and LOINC and whether the D-dimer result was elevated using SNOMED CT. The number of ED encounters, PTP use, and diagnosis of PE using different codes were determined. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value for selected codes were calculated. Over a 4-month study period, 270,214 encounters were included from 38 EDs. 11,794 ED encounters with CTPAs during the study period, using the site-specific codes were identified. The comprehensive value set had a PPV of 63.4%. Restricting the CTPA value set to CPT code 71275 or LOINC 88322-3 improved the PPV to 82% with 100% sensitivity and 99% specificity. The restricted value set captured the highest proportion of relevant site-specific codes. D-dimer values were identified using LOINC codes 48065-7 and 91556-1 at Site 1 and 48067-3 at Site 2. SNOMED CT codes were not used at any site to identify elevated D-dimer results. Different D-dimer tests with different normal ranges were used at each site, and only one site provided an abnormal flag for D-dimer results. Heterogeneity in the use of nationally standardized codes for labs and imaging tests limits the ability to measure and compare quality across health care organizations for CTPA and D-dimer results. Restricting the identification of CTPA to CPT Code 71275 or LOINC 88322-3 resulted in high sensitivity and specificity, but false positives remain. Additionally, coding for an abnormal D-dimer test result is not standardized across institutions. Therefore, the currently available value sets cannot be used to develop eCQMs whose aim is to evaluate whether CTPA is ordered appropriately based on the PTP risk level and laboratory testing.

Abstract The prevalence of steatotic liver disease (SLD) is rising globally, presenting a significant public health challenge. While non-contrast computed tomography (CT) scans hold promise for the opportunistic detection of SLD, their full potential has not been systematically explored. Clinical indicators, such as serum data, are crucial for monitoring the progression of SLD. We introduce a novel Multimodal AI for Opportunistic Steatotic Liver Disease Screening (MAOSS), which integrates demographic data, serum tests, conventional CT biomarkers, and deep learning-derived CT image features to enhance model performance and explainability. MAOSS employs an innovative learning strategy, trained on a large-scale dataset (n = 1,783) encompassing both histopathologically-confirmed SLD and radiologically-reported steatosis cases. The model underwent extensive retrospective validation using steatosis grades derived from both histology and MRI-PDFF across internal and external datasets (n = 913), as well as real-world patient data (n = 18,504). It accurately detected mild to severe steatosis (≥ S1) with AUCs ranging from 0.903 to 0.929. Notably, MAOSS significantly improved the overall AUC for radiologists (n = 11) from 0.709 to 0.798 (p < 0.001). Furthermore, MAOSS was applied to optimize the standard clinical pathway for a simulated primary risk screening of SLD patients (n = 1,192), resulting in the identification of 31% more patients at risk of steatohepatitis and liver fibrosis progression, highlighting its potential to enhance SLD detection and management.

Background/Objectives: Stereotactic body radiation therapy (SBRT) for skull base reirradiation is particularly challenging, as patients have already received substantial radiation doses to the region, and nearby normal organs may have approached their tolerance limit from prior treatments. In this study, we reviewed the characteristics and capabilities of four advanced external beam radiation delivery systems and four modern treatment planning systems and evaluated the treatment plan quality of each technique using skull base reirradiation patient cases. Methods: SBRT plans were generated for sixteen skull base reirradiation patients using four modalities: the GK plan for the Elekta Leksell Gamma Knife Perfexion/ICON, the CyberKnife (CK) plan for the Accuray CyberKnife, the intensity-modulated proton therapy (IMPT) plan for the Hitachi ProBeat-FR proton therapy machine, and the volumetric-modulated arc therapy (VMAT) plan for the Varian TrueBeam STx. These plans were evaluated and compared using two novel gradient indices in addition to traditional dosimetry metrics for targets and organs at risk (OARs). The steepest border gradient quantified the percent prescription dose fall-off per millimeter at the boundary between the target and adjacent critical structures. This gradient index highlighted the system’s ability to spare nearby critical OARs. The volume gradient assessed the extent of dose spread outside the target toward the patient’s body. Results: All plans achieved comparable target coverage and conformity, while IMPT and VMAT demonstrated significantly better uniformity. The GK plans exhibited the highest border gradient, up to 20.9%/mm, followed by small-spot-size IMPT plans and CK plans. Additionally, IMPT plans showed the benefit of reduced dose spread in low-dose regions and the lowest maximum and mean doses to the brainstem and carotid artery. Conclusions: The advanced external beam radiotherapy modalities evaluated in this study are well-suited for SBRT in skull base reirradiation, which demands precise targeting of tumors with highly conformal doses and steep dose gradients to protect nearby normal structures.

BACKGROUND AND OBJECTIVES: Aggressive resection for primary tumors of the spine are associated with a high rate of adverse events (AEs), but the impact of AEs on patient-reported outcomes (PROs) remains unknown and is critical to the shared decision-making. Our primary objective was to assess the impact of surgical AEs on PROs using an international registry. Assessing the impact on clinical outcomes and identifying risk factors for AEs were our secondary objectives. METHODS: Patients who underwent surgery for a primary spinal tumor were selected through the Primary Tumor Research and Outcomes Network. Our primary outcome was the impact of AEs on PROs at 3 and 12 months after surgery (measured with Spinal Oncology Study Group Outcomes Questionnaire, Short-Form 36, and EuroQol 5 Dimension). We also assessed the impact on clinical outcomes (local control, surgical margins, readmission, reoperation, and mortality). We stratified our results according to severity of AEs, histology, and type of resection. RESULTS: 374 patients met inclusion criteria (219 males/155 females). The mean age of the cohort was 48.7 years. The most frequent histology was chordoma (37.3%) followed by chondrosarcoma (8.8%). Sixty-seven patients (17.9%) experienced at least 1 intraoperative AE and 117 patients (31.3%) had at least 1 postoperative AE within 3 months. Overall, 159 patients (42.5%) experienced AEs. The readmission rate was significantly higher in patients who experienced AEs (Any AE: 10.1% vs no AE: 1.9% within 3 months; P = <0.001). PROs were not significantly affected by AEs in most questionnaires. Local control, risk of reoperation, mortality, and achieving preplanned margins were similar between AE groups. CONCLUSION: The rate of surgical AEs is considerable in this population. Surgical AEs seem to be associated with a higher number of readmissions, but do not seem to result in significant differences in PROs or in a higher risk of reoperation, mortality, and failure to achieve preplanned margins.

Abstract Background: Waist circumference (WC) and its allometric counterpart, “a body shape index” (ABSI), are risk factors for colorectal cancer; however, it is uncertain whether associations with these body measurements are limited to specific molecular subtypes of the disease. Methods: Data from 2,772 colorectal cancer cases and 3,521 controls were pooled from four cohort studies within the Genetics and Epidemiology of Colorectal Cancer Consortium. Four molecular markers (BRAF mutation, KRAS mutation, CpG island methylator phenotype, and microsatellite instability) were analyzed individually and in combination (Jass types). Multivariable logistic and multinomial logistic models were used to assess the associations of WC and ABSI with overall colorectal cancer risk and, in case-only analyses, to evaluate heterogeneity by molecular subtype, respectively. Results: Higher WC (ORper 5 cm = 1.06, 95% confidence interval, 1.04–1.09) and ABSI (ORper 1-SD = 1.07, 95% confidence interval, 1.00–1.14) were associated with elevated colorectal cancer risk. There was no evidence of heterogeneity between the molecular subtypes. No difference was observed regarding the influence of WC and ABSI on the four major molecular markers in proximal colon, distal colon, and rectal cancers, as well as in early- and late-onset colorectal cancers. Associations did not differ in the Jass-type analysis. Conclusions: Higher WC and ABSI were associated with elevated colorectal cancer risk; however, they do not differentially influence all four major molecular mutations involved in colorectal carcinogenesis but underscore the importance of maintaining a healthy body weight in colorectal cancer prevention. Impact: The proposed results have potential utility in colorectal cancer prevention.

AimsWe compiled two cohorts of colorectal adenosquamous carcinoma (ASC) to describe its histologic and molecular aspects using modern parameters to compare them with literature reports using meta‐analysis of cohorts and individual case series.Methods and ResultsWe identified 53 colorectal ASC from 19 North American academic medical centres, in addition to national database reports on 94 Dutch cases. We analysed available clinical, histologic, and immunohistochemical features and patient outcome. ASC comprised 0.02% of colorectal cancers in the Dutch database. The median cohort patient ages at resection were 65 and 69 years (North American and Dutch cohorts, respectively), with a roughly equal male:female ratio. The squamous component represented between 5% and 95% of each tumour, with a median of 50%. Tumour‐infiltrating lymphocytes (TILs) were generally low (66%), and tumour budding was often Bd1 (64%). Most cases were pT3 (55%) or pT4 (40%), with nodal metastases in more than half (58%). Twenty‐three cases (43%) metastasized distantly, most commonly to the liver. Mismatch repair (MMR) deficiency was identified in 34% of the cases. Follow‐up was available for 48 patients; 13 (27%) had recurrent disease and 29 (60%) died. A total of 31 patients progressed, with median time to progression of 18 months. Available data for the Dutch cohort revealed largely similar findings, as did review of cases in the literature.ConclusionColorectal ASC usually presents at an advanced stage. Despite high rates of MMR deficiency and low tumour budding, TILs were generally low, and there is a high recurrence rate and poor prognosis.

Purpose: This study compares the performance of small, fine-tuned LLMs with the large original models on two radiation oncology clinical tasks: treatment modality selection and ICD-10 code prediction. Methods: We used data of 403 treatment modality selection cases and 377 ICD-10 code prediction cases. The models used were fine-tuned LLaMA 2 7B and Mistral 7B, as well as the larger LLaMA 3.1 70B and 405B models for comparison. We evaluated the accuracy of all models on these two tasks. Results: Both fine-tuned LLaMA 2 7B and Mistral 7B outperformed LLaMA 3.1 70B and 405B in treatment modality selection task. The fine-tuned LLaMA 2 7B and Mistral 7B models achieved accuracy comparable to the larger models in ICD-10 code prediction. Conclusions: Small models, fine-tuned with radiation oncology data, can achieve performance comparable to-or even surpass-that of larger models.

484 Background: The histopathological growth patterns (HGPs) of liver metastasis reflect the complicated and varied interactions between tumor cells and the host microenvironment. We studied HGPs of gastric cancer liver metastasis (GCLM) and sought to predict them using radiomic and clinical variables. Methods: Hepatectomy was performed in 62 patients with GCLM at our university hospital between 2011 and 2021. HGPs were evaluated according to international consensus guidelines, and were classified as either desmoplastic HGP (dHGP) or non-dHGP. CD4, CD8, VEGF and CD31 were analyzed by immunohistochemistry. Eight hundred and fifty-one radiomics features were extracted from CT portal venous phase images and the optimal radiomics signature was determined by univariate analysis and LASSO regression. Multivariable regression analyses and ROC curves were used to assess the predictive performance of HGPs with a radiomic and clinical combined model. Results: Among 62 patients, 14 were categorized as dHGP, and 48 as non-dHGP. Non-dHGP vs. d-HGP was associated with higher tumor N stage and CEA level, and with decreased CD8 + T cells, and lower VEGF and CD31 expression. HGP status was independently associated with patient cancer-specific survival (CSS) (P＜0.05) multivariately.Analysis of percent relative contribution revealed that HGP ranked first for CSS (35.9%), ahead of T (28.9%) and N (28.9%) stage. A model with combined radiomic and clinical features demonstrated robust performance with area under the curve (AUC) values of 0.993 and 0.933 in the training and validation sets, respectively. Conclusions: Non-invasive determination of radiomic and clinical features was shown to predict HGPs types. Compared to dHGP, GCLM with non-dHGP exhibited significantly lower immune cell infiltration and decreased angiogenesis. Among features examined in GCLM, HGP was the most robust for prognostication. Univariate and multivariate analysis of prognostic factors for cancer-specific survival. Characteristics Univariate analysis Multivariate rergression analysis HR (95%CI) P HR (95%CI) P Gender ( Women vs Men ) 1.13(0.61-2.09) 0.71 Age ( >60years vs ≤60years) 1.08(0.60-1.70) 0.98 HGP ( non-desmoplastic HGP vs desmoplastic HGP ) 3.63(1.78-7.41) 0.00 3.48(1.37-8.88) 0.01 T stage ( T3-T4 vs T1-T2 ) 2.03(1.08-3.80) 0.03 2.29(1.15-4.58) 0.02 N stage ( N1-N3 vs N0 ) 3.46(1.66-7.23) 0.00 2.54(1.17-5.56) 0.02 CEA ( >5ng/ml vs ≤5ng/ml ) 2.37(1.25-4.48) 0.01 1.56(0.70-3.48) 0.27

483 Background: Heterogeneity exists in recurrence and survival among patients with intestinal-type gastric cancer (IGC). Nucleotyping is the alterations in chromatin organization. We sought to identify the prognostic utility of nucleotyping, tumor stroma ratio (TSR) and DNA ploidy in these patients. Methods: Radical resection of gastric carcinoma (> 15 LNs removed) was performed in 283 patients (stage I, n=73; stage II, n=102; stage III, n=108) at our university hospital between 2010 and 2018. Automated image cytometry tools were used to quantitatively measure nucleotyping, TSR and DNA ploidy. Cutpoints of nucleotyping and TSR were 0.044 and 50%, respectively. Associations of these features with clinicopathological characteristics, time-to-recurrence (TTR) and cancer specific survival (CSS) were analyzed by univariate and multivariate analysis. Results: Among 283 patients, chromatin heterogeneity, high TSR and non-diploid were significantly associated with larger tumor size, higher T and N stage, and poorer TTR and CSS at 5 years. In a Cox multivariable analysis, nucleotyping and TSR were independently associated with TTR and CSS (P＜0.05) (Table). The top three contributors to TTR were N stage (33.9%), nucleotying (29.7%) and TSR (25.5%). The top three contributors to CSS were N stage (35.6%), nucleotying (29.7%) and TSR (18.0%). As a combined variable, the percent relative contribution of nucleotyping and TSR ranked first for TTR and CSS (56.0%, 50.0%), ahead of N stage (33.4%, 34.6%) and TILs (8.4%, 10.6%). Conclusions: Nucleotyping and TSR were each significantly and independently associated with TTR and CSS, and their combined variable was more strongly prognostic than either alone. Cox multivariable analysis of TTR and CSS. Features TTR HR (95%CI) TTR P value CSS HR (95%CI) CSS P value Tumor size (>3cm vs ≤3cm) 1.10 (0.48-2.54) 0.83 0.77 (0.35-1.70) 0.56 T stage (T3-T4 vs T1-T2) 1.07 (0.46-2.49) 0.87 1.41 (0.53-3.76) 0.49 N stage (N2N3 vs N0N1) 2.84 (1.39-5.78) <0.01 7.69 (3.37-17.55) <0.01 Adjuvant Therapy (with vs without) 1.32 (0.72-2.40) 0.37 0.85 (0.48-1.52) 0.59 TB (high vs low) 1.29 (0.62-2.69) 0.50 1.61 (0.85-3.07) 0.15 TILs（low vs high） 1.73 (0.96-3.13) 0.07 1.90 (1.06-3.40) 0.03 Nucleotyping (Chromatin heterogeneous vs Chromatin homogeneous) 2.19 (1.21-3.99) 0.01 3.44 (1.96-6.03) <0.01 TSR (high vs low) 2.12 (1.16-3.82) 0.01 2.51 (1.43-4.41) <0.01 DNA ploidy (Non-diploid vs Diploid) 1.20 (0.55-2.63) 0.65 0.98 (0.44-2.15) 0.95 1 TTR, time-to-recurrence; 2 CSS, cancer specific survival.