Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation.

Despite the growing prominence of transformers in medical image segmentation, their application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal attention has been paid to domain shift analysis and uncertainty assessment, critical for safely implementing computer-aided diagnosis (CAD) systems. Domain shift in medical imagery refers to differences between the data used to train a model and the data evaluated later, arising from variations in imaging equipment, protocols, patient populations, and acquisition noise.

Development of a novel computational technique to create DNA and cell geometrical models for Geant4-DNA.

Background: This study aimed to develop a novel human cell geometry for the Geant4-DNA simulation toolkit that explicitly incorporates all 23 chromosome pairs of the human cell. This approach contrasts with the existing, default human cell, geometrical model, which utilizes a continuous Hilbert curve.

Methods: A Python-based tool named "complexDNA" was developed to facilitate the design of both simple and complex DNA geometries.

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches.

Investigation of Machine and Deep Learning Techniques to Detect HPV Status.

Background: This study investigated alternative, non-invasive methods for human papillomavirus (HPV) detection in head and neck cancers (HNCs). We compared two approaches: analyzing computed tomography (CT) scans with a Deep Learning (DL) model and using radiomic features extracted from CT images with machine learning (ML) models.

Methods: Fifty patients with histologically confirmed HNC were included.

Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion.

Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples.

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.

The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process.

Cross-modal tumor segmentation using generative blending augmentation and self-training.

Objectives: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities.

Methods: We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique called Generative Blending Augmentation (GBA).

PET/CT-Based Radiogenomics Supports KEAP1/NFE2L2 Pathway Targeting for Non-Small Cell Lung Cancer Treated with Curative Radiotherapy.

In lung cancer patients, radiotherapy is associated with a increased risk of local relapse (LR) when compared with surgery but with a preferable toxicity profile. The KEAP1/NFE2L2 mutational status (Mut) is significantly correlated with LR in patients treated with radiotherapy but is rarely available. Prediction of Mut with noninvasive modalities could help to further personalize each therapeutic strategy.

Contrastive image adaptation for acquisition shift reduction in medical imaging.

The domain shift, or acquisition shift in medical imaging, is responsible for potentially harmful differences between development and deployment conditions of medical image analysis techniques. There is a growing need in the community for advanced methods that could mitigate this issue better than conventional approaches. In this paper, we consider configurations in which we can expose a learning-based pixel level adaptor to a large variability of unlabeled images during its training, i.

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches.

Multi-modal medical Transformers: A meta-analysis for medical image segmentation in oncology.

Multi-modal medical image segmentation is a crucial task in oncology that enables the precise localization and quantification of tumors. The aim of this work is to present a meta-analysis of the use of multi-modal medical Transformers for medical image segmentation in oncology, specifically focusing on multi-parametric MR brain tumor segmentation (BraTS2021), and head and neck tumor segmentation using PET-CT images (HECKTOR2021). The multi-modal medical Transformer architectures presented in this work exploit the idea of modality interaction schemes based on visio-linguistic representations: (i) single-stream, where modalities are jointly processed by one Transformer encoder, and (ii) multiple-stream, where the inputs are encoded separately before being jointly modeled.

["New Modalities in Cancer Imaging and Therapy" XVth edition of the workshop organized by the network "Tumor Targeting, Imaging, Radiotherapies" of the Cancéropôle Grand-Ouest, 5-8 October 2022, France].

The fifteenth edition of the international workshop organized by the "Tumour Targeting and Radiotherapies network" of the Cancéropôle Grand-Ouest focused on the latest advances in internal and external radiotherapy from different disciplinary angles: chemistry, biology, physics, and medicine. The workshop covered several deliberately diverse topics: the role of artificial intelligence, new tools for imaging and external radiotherapy, theranostic aspects, molecules and contrast agents, vectors for innovative combined therapies, and the use of alpha particles in therapy.

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables.

Monte Carlo Dosimetry Validation for X-Ray Guided Endovascular Procedures.

Background: Endovascular treatment is continuously gaining ground in vascular surgery procedures. However, current patient radiation dose estimation does not take into account the exact patient morphology and organs' composition. Monte Carlo (MC) simulation can accurately estimate the dose by recreating the irradiation process generated during X-ray-guided interventions.

Fast dose calculation in x-ray guided interventions by using deep learning.

Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring systems estimate the skin dose based in dose metrics such as the reference air kerma. However, these approximations do not take into account the exact patient morphology and organs composition.

Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images.

Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells.

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT.

This paper presents an overview of the third edition of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. The challenge comprises two tasks related to the automatic analysis of FDG-PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. is the fully automatic segmentation of H&N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images.

Design of a generic method for single dual-tracer PET imaging acquisition in clinical routine.

Using different tracers in positron emission tomography (PET) imaging can bring complementary information on tumor heterogeneities. Ideally, PET images of different tracers should be acquired simultaneously to avoid the bias induced by movement and physiological changes between sequential acquisitions. Previous studies have demonstrated the feasibility of recovering separated PET signals or parameters of two or more tracers injected (quasi-)simultaneously in a single acquisition.

Multicentric development and evaluation of F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer.

Purpose: To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using F-FDG PET/CT and MRI radiomics combined with clinical parameters.

Methods: We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated.

Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival.

Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy.

Introduction: The standard of care for people with locally advanced lung cancer (LALC) who cannot be operated on is (chemo)-radiation. Despite the application of dose constraints, acute pulmonary toxicity (APT) still often occurs. Prediction of APT is of paramount importance for the development of innovative therapeutic combinations.

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches.