Positron emission tomography imaging biomarker and artificial intelligence for the characterization of solitary pulmonary nodule.

Background: The characterization of solitary pulmonary nodules (SPNs) as malignant or benign remains a diagnostic challenge using conventional imaging parameters. The literature suggests using combined Positron Emission Tomography (PET) and Computed Tomography (CT) to characterise a SPN. Radiomics and machine learning are other promising technologies which can be utilised to characterise the SPN.

Pragmatic Patient Choice-Driven Radical Treatment Approach (PRAGRAD) for Very Advanced Unresectable Oral Cavity Cancers.

Purpose: The treatment of locally advanced unresectable oral cavity cancers (OCCs) is challenging, with limited consensus on optimal management and poor outcomes. Our clinical practice identified a subset of unresectable OCC patients who respond favorably to aggressive alternate treatment with chemotherapy and radiation therapy. We propose a systematic design for optimal selection method that is patient choice-driven while attempting to managing unresectable OCC with radical therapy approach.

From Federated Learning to Publicly Facing Platform; Proof-of-Concept of the STRONG AYA Information Portal.

Information on health data is often shrouded in exclusivity and sharing of such information is normally difficult and time-locked for numerous reasons. Federated learning (FL) regularly accelerates knowledge sharing, yet it is not fully exploited to alleviate overall data exclusivity and time constraints. Here, we present a proof-of-concept for a public FL-based info portal of the STRONG AYA Initiative.

A Governance Framework for Federated Learning Projects in Healthcare.

Federated Learning is becoming more widely used. However, a governance framework is needed to make sure this technology is used safely. In this work, we present a governance framework for Federated Learning project and show how often we have applied the accompanying agreements.

Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.

Background: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develop and validate a deep learning model using a novel voxel-level radiomics approach to predict pCR based on preoperative CT images.

Methods: In this multicenter, retrospective study, 741 patients with ESCC who underwent nICT followed by radical esophagectomy were enrolled from three institutions.

Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study.

Background: The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data.

Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High-Risk Individuals.

Background: Lung cancer (LC) is the top cause of cancer deaths globally, prompting many countries to adopt LC screening programs. While screening typically relies on age and smoking intensity, more efficient risk models exist. We devised a Bayesian network (BN) for LC detection, testing its resilience with varying degrees of missing data and comparing it to a prior machine learning (ML) model.

Whole lung radiomic features are associated with overall survival in patients with locally advanced non-small cell lung cancer treated with definitive radiotherapy.

Background: Several studies have suggested that lung tissue heterogeneity is associated with overall survival (OS) in lung cancer. However, the quantitative relationship between the two remains unknown. The purpose of this study is to investigate the prognostic value of whole lung-based and tumor-based radiomics for OS in LA-NSCLC treated with definitive radiotherapy.

Prognostic and Predictive Biomarkers of Oligometastatic NSCLC: New Insights and Clinical Applications.

This review discusses the current data on predictive and prognostic biomarkers in oligometastatic NSCLC and discusses whether biomarkers identified in other stages and widespread metastatic disease can be extrapolated to the oligometastatic disease (OMD) setting. Research is underway to explore the prognostic and predictive value of biological attributes of tumor tissue, circulating cells, the tumor microenvironment, and imaging findings as biomarkers of oligometastatic NSCLC. Biomarkers that help define true OMD and predict outcomes are needed for patient selection for oligometastatic treatment, and to avoid futile treatments in patients that will not benefit from locoregional treatment.

A Bayesian Network Approach to Lung Cancer Screening: Assessing the Impact of Data Quantity, Quality, and the Combination of Data from Danish Electronic Health Records.

: Lung cancer (LC) is the leading cause of cancer mortality, making early diagnosis essential. While LC screening trials are underway globally, optimal prediction models and inclusion criteria are still lacking. This study aimed to develop and evaluate Bayesian Network (BN) models for LC risk prediction using a decade of data from Denmark.

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.

Purpose: Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored.

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.

The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice.

Skeletal muscle is independently associated with grade 3-4 toxicity in advanced stage pancreatic ductal adenocarcinoma patients receiving chemotherapy.

Background: Patients with advanced-stage pancreatic ductal adenocarcinoma (PDAC) are regularly treated with FOLFIRINOX, a chemotherapy regimen based on 5-fluorouracil, irinotecan and oxaliplatin, which is associated with high toxicity. Dosing of FOLFIRINOX is based on body surface area, risking under- or overdosing caused by altered pharmacokinetics due to interindividual differences in body composition. This study aimed to investigate the relationship between body composition and treatment toxicity in advanced stage PDAC patients treated with FOLFIRINOX.

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Background: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy.

External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.

Objectives: Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images.

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.

Objectives: Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.

Methods: This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023.

Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase.

A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients.

Predictive modelling of cancer outcomes using radiomics faces dimensionality problems and data limitations, as radiomics features often number in the hundreds, and multi-institutional data sharing is ()often unfeasible. Federated learning (FL) and feature selection (FS) techniques combined can help overcome these issues, as one provides the means of training models without exchanging sensitive data, while the other identifies the most informative features, reduces overfitting, and improves model interpretability. Our proposed FS pipeline based on FL principles targets data-driven radiomics FS in a multivariate survival study of non-small cell lung cancer patients.

Getting real about synthetic data ethics : Are AI ethics principles a good starting point for synthetic data ethics?

Synthetic data promises to be a viable alternative when data collection and data sharing may not be feasible or cost effective, but it raises distinct ethical issue that merit serious consideration. [Image: see text]

Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital.

Objectives: Radiation therapy for lung cancer requires a gross tumour volume (GTV) to be carefully outlined by a skilled radiation oncologist (RO) to accurately pinpoint high radiation dose to a malignant mass while simultaneously minimizing radiation damage to adjacent normal tissues. This is manually intensive and tedious however, it is feasible to train a deep learning (DL) neural network that could assist ROs to delineate the GTV. However, DL trained on large openly accessible data sets might not perform well when applied to a superficially similar task but in a different clinical setting.

A PET/CT radiomics model for predicting distant metastasis in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study.

Objectives: Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT.

SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation.

Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation.

Image based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization.

In the past decade, there has been a sharp increase in publications describing applications of convolutional neural networks (CNNs) in medical image analysis. However, recent reviews have warned of the lack of reproducibility of most such studies, which has impeded closer examination of the models and, in turn, their implementation in healthcare. On the other hand, the performance of these models is highly dependent on decisions on architecture and image pre-processing.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic.