Citations

Objective: To develop a predictive model for pathological complete response (pCR) after total neoadjuvant therapy (TNT) to inform selection for watch-and-wait (W/W). Summary Background Data: Patient selection for W/W after TNT for locally advanced rectal cancer remains challenging. Methods: An ensemble of tabular foundation models was fine-tuned in adults with clinical stage II or III microsatellite stable primary rectal adenocarcinoma undergoing TNT and total mesorectal excision (TNT+TME) from 2018-2023 to predict pCR, using pre-TNT, post-TNT and pre-TME variables. This model was externally validated on patients having TNT and W/W (TNT+W/W) to predict persistent clinical complete response (pcCR; the absence of local regrowth, distant metastases, or persistent near-cCR). Area under the receiver operator curve (AUROC), area under the precision-recall curve (AUPRC), and Brier score were calculated with 95% confidence intervals (CI). Results: Among 308 patients that underwent TNT+TME (median age 56; 40% female), the model predicted pCR with an AUROC 0.71 (95% CI 0.65-0.77), AUPRC 0.44 (95% CI 0.35-0.57), and was well calibrated with a Brier score of 0.17 (95% CI 0.15-0.20). At external validation in a cohort of 83 patients that are being managed with TNT+W/W (median age 57; 37% female), the model predicted pcCR with an AUROC 0.69 (95% CI 0.57-0.82), AUPRC 0.90 (95% CI 0.82-0.96), and Brier score of 0.30 (95% CI 0.26-0.33), improving to 0.17 with recalibration. Conclusions: This novel predictive model demonstrated good discrimination and calibration for pCR after TNT+TME with utility in TNT+W/W for pcCR after appropriate recalibration, supporting its application for W/W patient selection.

Abstract Rationale In fungal endemic regions, differentiating benign from malignant pulmonary nodules on CT scans remains a major diagnostic challenge. Coccidioides nodules frequently resemble malignant lesions on CT and PET imaging, presenting significant challenges for differentiation, even among radiologists. Consequently, the benign biopsy and resection rate, especially in the Southwestern US, remains high. We previously developed [DOI:10.1371/journal.pone.0196910] and externally validated [DOI:10.1183/13993003.02485-2020] the Benign versus aggRessive nODule Evaluation using Radiomic Stratification (BRODERS) machine learning (ML) classifier to non-invasively distinguish between benign and malignant pulmonary nodules with an AUC of 0.87. However, its performance in certain benign nodules with malignant surface and texture characteristics like in coccidioidomycosis had a suboptimal AUC of 0.65. In this study, we redesigned our radiomics model by including numerous clinical features. Methods In this retrospective study, 113 pulmonary nodules (66 Coccidioides and 47 adenocarcinoma spectrum) detected on CTs with ground truth pathology diagnosis were identified by screening for key terms like “nodule”, “cocci”, and “adenocarcinoma” from both CT and pathology reports using a large language model trained on clinical textual data (Illuminate®). Clinical features such as demographics, smoking history, and laboratory studies were extracted by trained clinicians from the electronic medical record. All nodules were segmented and radiomic features extracted using a semi-automated seed growing algorithm previously described [DOI: 10.1097/JTO.0b013e3182843721]. The database was randomly split into an 80%-20% development and hold-out set prior to ML modeling. Feature selection for variables was conducted utilizing Elastic Net Regression, which provides a balanced approach between Lasso and Ridge regression. A logistic regression model was developed using the selected features and ground truth information of the training cohort. The true performance of the model was tested on the hold-out set. Results 160 radiomics and 22 clinical features were extracted. Elastic Net regression identified 44 features that contributed the most. Some of these clinical features include presence of satellite lesions, smoking history, and presence of positive IgG coccidiomycosis serology. The AUC of the ROC curve for the hold-out test set was 0.86. Conclusions The clinical features of significance that emerged during multimodal modeling are explainable. Designing explainable ML models is important, especially in the Southwestern US where Coccidioides nodules can appear to be malignant to both human experts and ML models. Although coccidioidomycosis serologies alone lack sensitivity and specificity to diagnose Coccidioides nodules, integrating them with a multimodal ML model can improve differentiation between benign Coccidioides and malignant adenocarcinoma nodules. This abstract is funded by: None

Abstract Background Recently, a posterior pathway for fluid drainage from the retina to the meningeal lymphatics in the optic nerve (ON) sheath was identified in rodents using intravitreal imaging tracers directly injected into the ocular-globe. Fluid and solute clearance along this pathway may be associated with many diseases. However, intravitreal tracers are rarely used in clinical imaging. As intravenous Gadolinium-based-contrast-agent (GBCA) can enter the globe via the blood-ocular-barriers, it may provide an alternative approach to image this pathway. Purpose To establish a clinically feasible intravenous GBCA-based MRI approach for tracking fluid and solute transport along the posterior lymphatic pathway in the ocular glymphatic system. Materials&Methods This prospective study was conducted from March 2021 to September 2022 in healthy participants. Dynamic-susceptibility-contrast-in-the-CSF (cDSC) MRI was performed before, immediately and 4 hours after intravenous-GBCA administration to track GBCA distribution in aqueous humor (AH) and cerebrospinal fluid (CSF) in regions-of-interest (ROIs) in the globe (anterior-cavity, vitreous-body), in the intraorbital and extraorbital ON, and in the intracranial CSF space proximal to the ON (chiasmatic-cistern, interpeduncular-cistern). Kruskal–Wallis tests with post-hoc Dunn’s tests were used for group comparisons. Results Sixteen healthy participants (mean age±SD: 51±21 years, 5 men) were recruited. Intravenous-GBCA enhancement was observed in all ROIs immediately after injection. At 4-hour-post-GBCA, the vitreous body showed a trend of smaller enhancement area (55±11% versus 49±11%, P =.14) and lower GBCA-concentration (0.044±0.014 versus 0.028±0.010 mmol/L, P =.07) compared to immediate-post-GBCA. The intraorbital ON showed more widespread enhancement (39±5% versus 59±6%, P =.01) and significantly higher GBCA-concentration (0.023±0.009 versus 0.059±0.015 mmol/L, P <.001) at 4-hour-post-GBCA. Conclusion Dynamic fluid and solute transportation along the posterior lymphatic pathway in the ocular glymphatic system in healthy participants was measured by tracking intravenous-GBCAs entering the globe via the blood-ocular-barriers using cDSC-MRI. Summary Statement Dynamic fluid and solute transportation along the posterior lymphatic pathway in the ocular glymphatic system in healthy participants was measured by tracking intravenous-Gadolinium-based-contrast-agents entering ocular-globe via blood-ocular-barriers using dynamic-susceptibility-contrast-in-the-CSF (cDSC)-MRI. Key Results In this prospective study of sixteen participants, Gadolinium-based-contrast-agent (GBCA)-induced signal changes were detected in the aqueous-humor and cerebrospinal fluid immediately following intravenous administration using dynamic-susceptibility-contrast-in-the-CSF (cDSC)-MRI. At 4-hour-post-GBCA, the vitreous-body showed a trend of smaller enhancement area (55±11% versus 49±11%, P =.14) and lower GBCA-concentration (0.044±0.014 versus 0.028±0.010 mmol/L, P =.07) compared to immediate-post-GBCA. The intraorbital-optic-nerve showed more widespread enhancement (39±5% versus 59±6%, P =.01) and higher GBCA-concentration (0.023±0.009 versus 0.059±0.015 mmol/L, P <.001) at 4-hour-post-GBCA.

ABSTRACT Background The assessment of gastrointestinal (GI) motility disorders is limited by the invasive, resource‐intensive, and subjective nature of current testing. Artificial Intelligence (AI) is being applied to address these challenges. This review aimed to comprehensively identify, appraise, and synthesize the spectrum of AI applications in GI motility diagnostics. Methods A systematic search of PubMed, Embase, Scopus, Web of Science, and Medline for original studies was conducted in March 2025. Results were narratively synthesized and grouped by anatomic region. Key Results Of 1383 articles, 90 primary studies met the inclusion criteria. In the esophagus ( n = 31), the primary focus was automation of High‐Resolution Esophageal Manometry (HREM), pH impedance, and Functional Luminal Imaging Probe (FLIP) panometry analysis. In the gastroduodenum ( n = 21), studies looked to improve diagnostic accuracy and interpretability of legacy electrogastrography (EGG). In the small and large intestine ( n = 19), AI was used to determine transit time and predict post‐operative ileus. In the anorectum ( n = 9), tools focused on standardizing manometry interpretation. Review of pan‐GI applications ( n = 10) highlighted AI's application to wireless capsule endoscopy and bowel sounds analysis. While promising, all areas need more rigorous research and external validation before widespread deployment. Conclusions and Inferences AI will play an essential role in improving and automating the interpretation of GI motility diagnostics. However, even the most promising models require large‐scale prospective validation before clinical implementation. Trial Registration PROSPERO (ID: 1128662)

ABSTRACT Background Body surface gastric mapping (BSGM) non‐invasively assesses gastric myoelectrical activity along with real‐time symptom reporting. In adults, the development of normative intervals has underpinned new explanatory phenotypes, aiding clinical decision‐making. This study established normative reference intervals for adolescent BSGM metrics. Methods Healthy adolescents aged 12–17 years with a BMI ≤ 35 kg/m 2 were recruited from New Zealand, Australia, and the United States. BSGM using Gastric Alimetry (Alimetry, New Zealand) involved a 30‐min fast, followed by a 480‐kcal meal, and a 4‐h postprandial recording. Reference intervals were calculated for four validated metrics: Principal Gastric Frequency (PGF), body mass index (BMI)‐adjusted amplitude, Gastric Alimetry Rhythm Index (GA‐RI, indicating rhythm stability), and the fed: fasted amplitude ratio (ff‐AR). Results were reported at the median and 5th and/or 95th percentiles as appropriate. Key Results A total of 107 participants (52.8% female, median age 14 [IQR 13–16], median BMI 20.1 [IQR 18.75–22.40]) with mixed ethnicities were included. No substantive correlations were observed between BSGM metrics and demographics or anthropometric data. Therefore, a single set of normative reference intervals was established. Median PGF was 3.06 cycles per minute; reference interval 2.72–3.37. Median BMI‐adjusted amplitude was 37.80 μV; reference interval 20.0–72.0. Median GA‐RI was 0.51; reference interval ≥ 0.22. Median ff‐AR was 2.12; reference interval ≥ 1.0. Conclusions and Inferences This study presents normative reference intervals for BSGM spectral metrics in adolescent populations, informing the interpretation of tests in research and clinical practice.

Background Predictive models employing machine learning algorithms are increasingly being used in clinical decision making, and improperly calibrated models can result in systematic harm. We sought to investigate the impact of class imbalance correction, a commonly applied preprocessing step in machine learning model development, on calibration and modelled clinical decision making in a large real-world context. Methods A histogram boosted gradient classifier was trained on a highly imbalanced national dataset of >1.8 million patients undergoing surgery, to predict the risk of 90-day mortality and complications after surgery. Class imbalance correction strategies including random oversampling, synthetic minority oversampling technique, random under-sampling, and cost-sensitive learning were compared to the natural distribution ('natural'). Models were tested and compared with classification metrics, calibration plots, decision curve analysis, and simulated clinical impact analysis. Results The natural model demonstrated high performance (AUROC 0.94, 95% CI 0.94--0.95 for mortality; 0.84, 95% CI 0.84--0.85 for complications) and calibration (log loss 0.05, 95% CI 0.04--0.05 for mortality; 0.23, 95% CI 0.23--0.24 for complications). Class imbalance mitigation (CSL, ROS, RUS, and SMOTE) did not improve AUROC or AUPRC but increased recall and F1 scores at the expense of precision and accuracy. However, these methods severely compromised model calibration, leading to significant over-prediction of risks (up to a 62.8 % increase) as further evidenced by increased log loss across all mitigation techniques. Decision curve analysis and clinical scenario testing confirmed that the natural model provided the highest net benefit. Conclusion Class imbalance correction methods result in significant miscalibration, leading to possible harm whenused for clinical decision making.

ABSTRACT Background Many older adults undergo gastrointestinal (GI) cancer surgery, yet the relative impacts of advanced age, comorbidity, and frailty on postoperative mortality and failure to rescue (FTR) remain unclear. We aimed to compare the impacts of these factors on postoperative outcomes in a large population‐based cohort. Method We conducted a national retrospective linked database study of patients aged ≥ 65 years who underwent resection for GI or hepatobiliary cancer in Aotearoa New Zealand between 2005 and 2020. Age, comorbidity (C3 Comorbidity Score), and frailty (Hospital Frailty Risk Score) were examined as independent predictors of 90‐day mortality, complications, and FTR (death following a complication). Logistic regression models were adjusted for demographic and clinical covariates. Results Among 21,729 patients (mean age 75.8 ± 6.8 years), 49.3% experienced one or more complications and 6.1% died within 90 days (FTR rate of 12.3%). In adjusted models, each additional 5 years of age increased 90‐day mortality odds by 40% (adjusted odds ratio [aOR] 1.40 and 95% CI 1.34–1.46). Although higher comorbidity (C3 > 3) and frailty (Hospital Frailty Risk Score > 15) also independently raised mortality risk (aOR 1.98 and 2.04, respectively), age exerted the largest effect on mortality and FTR. Conclusion Chronological age, comorbidity, and frailty each predict worse outcomes following GI cancer surgery, but advanced age remains the dominant driver of 90‐day mortality and FTR. These findings underscore the need for risk stratification, shared decision‐making, and preoperative optimization as wells as intense surveillance for complications and early definitive care of older adults undergoing major oncologic operations.