Decision-tree derivation and external validation of a new clinical decision rule (DISCERN-FN) to predict the risk of severe infection during febrile neutropenia in children treated for cancer

Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features

OBJECTIVES

To develop and validate nomograms combining radiomics and semantic features to identify the invasiveness and histopathological risk stratification of thymic epithelial tumors (TET) using contrast-enhanced CT.

METHODS

This retrospective multi-center study included 224 consecutive cases. For each case, 6764 intratumor and peritumor radiomics features and 31 semantic features were collected. Multi-feature selections and decision tree models were performed on radiomics features and semantic features separately to select the most important features for Masaoka-Koga staging and WHO classification. The selected features were then combined to create nomograms for the two systems. The performance of the radiomics model, semantic model, and combined model was evaluated using the area under the receiver operating characteristic curves (AUCs).

RESULTS

One hundred eighty-seven cases (56.5 years ± 12.3, 101 men) were included, with 62 cases as the external test set. For Masaoka-Koga staging, the combined model, which incorporated five peritumor radiomics features and four semantic features, showed an AUC of 0.958 (95% CI: 0.912-1.000) in distinguishing between early-stage (stage I/II) and advanced-stage (III/IV) TET in the external test set. For WHO classification, the combined model incorporating five peritumor radiomics features and two semantic features showed an AUC of 0.857 (0.760-0.955) in differentiating low-risk (type A/AB/B1) and high-risk (B2/B3/C) TET. The combined models showed the most effective predictive performance, while the semantic models exhibited comparable performance to the radiomics models in both systems (p > 0.05).

CONCLUSION

The nomograms combining peritumor radiomics features and semantic features could help in increasing the accuracy of grading invasiveness and risk stratification of TET.

CRITICAL RELEVANCE STATEMENT

Peripheral invasion and histopathological type are major determinants of treatment and prognosis of TET. The integration of peritumoral radiomics features and semantic features into nomograms may enhance the accuracy of grading invasiveness and risk stratification of TET.

KEY POINTS

Peritumor region of TET may suggest histopathological and invasive risk. Peritumor radiomic and semantic features allow classification by Masaoka-Koga staging (AUC: 0.958). Peritumor radiomic and semantic features enable the classification of histopathological risk (AUC: 0.857).

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.

Machine learning based ischemia-specific stenosis prediction: A Chinese multicenter coronary CT angiography study

OBJECTIVES

To evaluate the performance of coronary computed tomography angiography (CCTA) derived characteristics including CT derived fractional flow reserve (CT-FFR) with FFR as a reference standard in identifying the lesion-specific ischemia by machine learning (ML) algorithms.

METHODS

The retrospective analysis enrolled 596 vessels in 462 patients (mean age, 61 years ± 11 [SD]; 71.4 % men) with suspected coronary artery disease who underwent CCTA and invasive FFR. The data were divided into training cohort, internal validation cohort, external validation cohorts 1 and 2 according to participating centers. All CCTA-derived parameters, which contained 10 qualitative and 33 quantitative plaque parameters, were collected to establish ML model. The Boruta and unsupervised clustering algorithm were implemented to select important and non-redundant parameters. Finally, the eight features with the highest mean importance were included for further ML model establishment and decision tree building. Five models were built to predict lesion-specific ischemia: stenosis degree from CCTA, CT-FFR, ΔCT-FFR, ML model and nested model.

RESULTS

Low-attenuation plaque, bend and lesion length were the main predictors of ischemia-specific lesions. Of 5 models, the ML model showed favorable discrimination for ischemia-specific lesions in the training and three validation sets (area under the curve [95 % confidence interval], 0.93 [0.90-0.96], 0.86 [0.79-0.94], 0.88 [0.83-0.94], and 0.90 [0.84-0.96], respectively). The nested model which combined the ML model and CT-FFR showed better diagnostic efficacy (AUC [95 %CI], 0.96 [0.94-0.99], 0.92 [0.86-0.99], 0.92 [0.86-0.99] and 0.94 [0.91-0.98], respectively; all P < 0.05), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were significantly higher than CT-FFR alone.

CONCLUSIONS

Comprehensive CCTA-derived multiparameter model could better predict the ischemia-specific lesions by ML algorithms compared to stenosis degree from CTA, CT-FFR and ΔCT-FFR. Decision tree can be used to predict myocardial ischemia effectively.

Consensus survey on the management of children with chemotherapy-induced febrile neutropenia and at low risk of severe infection

Our aim was to identify national consensus criteria for the management of children with chemotherapy-induced febrile neutropenia (FN), for evidence-based step-down treatment approaches for patients classified at low risk of severe infection. In 2018, a five-section, 38-item survey was e-mailed to all pediatric hematology and oncology units in France (n = 30). The five sections contained statements on possible consensus criteria for the (i) definition of FN, (ii) initial management of children with FN, (iii) conditions required for initiating step-down therapy in low-risk patients, (iv) management strategy for low-risk patients, and (v) antibiotic treatment on discharge. Consensus was defined by respondents' combined answers (somewhat agree and strongly agree) at 75% or more. Sixty-five physicians (participation rate: 58%), all specialists in pediatric onco-hematology, from 18 centers completed the questionnaire. A consensus was reached on 22 of the 38 statements, including the definition of FN, the criteria for step-down therapy in low-risk children, and the initial care of these patients. There was no consensus on the type and duration of antibiotic therapy on discharge. In conclusion, a consensus has been reached on the criteria for initiating evidence-based step-down treatment of children with FN and a low risk of severe infection but not for the step-down antimicrobial regimen.

Bacteremia in Pediatric Liver Transplant Recipients

BACKGROUND

We aimed to determine the frequency of bacteremia, septic shock and bacterial meningitis in pediatric liver transplant recipients (pLTRs) in the outpatient setting and to identify clinical factors associated with bacteremia.

METHODS

Multicenter retrospective study of pLTRs evaluated in the emergency department or outpatient clinic between 2010 and 2018 for suspected infection, defined as fever ≥38 °C or a blood culture obtained. We excluded patients with nontransplant immunodeficiency, multiorgan transplants or intestinal failure. The primary outcome was bacteremia; secondary outcomes included fluid-refractory septic shock, bacterial meningitis and antibiotic resistance. The unit of analysis was the encounter.

RESULTS

A total of 151 children had 336 encounters for infection evaluation within 2 years of transplant. Of 307 (91.4%) encounters with blood cultures, 17 (5.5%) had bacteremia, with 10 (58.8%) occurring within 3 months of transplant. Fluid-refractory septic shock and bacterial meningitis occurred in 7 out of 307 (2.8%) and 0 out of 307 encounters, respectively. Factors associated with bacteremia included closer proximity to transplant (<3 months) [odds ratio (OR): 3.6; 95% confidence interval (CI): 1.3-9.8; P = 0.01], shorter duration of illness (OR: 4.3; 95% CI: 1.5-12.0; P < 0.01) and the presence of a central venous catheter (CVC) (OR: 12.7; 95% CI: 4.4-36.6; P < 0.01). However, 5 (29.4%) encounters with bacteremia had none of these factors. Among Gram-positive pathogens, 1 out of 7 (14.2%) isolates were resistant to vancomycin. Among Gram-negative pathogens, 3 out of 13 (23.1%) isolates were resistant to 3rd generation cephalosporins.

CONCLUSIONS

Bacteremia was an important cause of infection within 2 years of pLTR. Clinical factors increased the risk of bacteremia. Further, large sample studies should derive multivariable models to identify those at high and low risk of bacteremia to optimize antibiotic use.

Immune signatures, testing, and management of febrile neutropenia in pediatric cancer patients

INTRODUCTION

Infectious complications, particularly invasive bacterial and fungal infections, are still a major cause of morbidity in pediatric cancer patients and are associated with significant mortality. Over the last few years, there has been much effort in defining risk groups to tailor antimicrobial therapy, and in establishing pediatric-specific guidelines for antimicrobial strategies.

AREAS COVERED

This review provides a critical overview of defining risk groups for infection, diagnostic work-up, antimicrobial prophylaxis, empirical therapy, and treatment of established infections.

EXPERT OPINION

To date, no generalizable risk prediction model has been established for pediatric cancer patients. There is growing interest in defining the impact of the individual genetic background on infectious complications. New diagnostic tools have been developed over the last few years, but they need to be validated in pediatric cancer patients. International, pediatric-specific guidelines for antimicrobial prophylaxis, empirical therapy, and treatment of established infections have recently been published and will harmonize antimicrobial strategies in the future.