[HTML][HTML] A systematic review of PET textural analysis and radiomics in cancer

M Piñeiro-Fiel, A Moscoso, V Pubul, Á Ruibal… - Diagnostics, 2021 - mdpi.com
Background: Although many works have supported the utility of PET radiomics, several
authors have raised concerns over the robustness and replicability of the results. This study …

[HTML][HTML] Post-treatment FDG PET-CT in head and neck carcinoma: comparative analysis of 4 qualitative interpretative criteria in a large patient cohort

J Zhong, M Sundersingh, K Dyker, S Currie… - Scientific reports, 2020 - nature.com
There is no consensus regarding optimal interpretative criteria (IC) for Fluorine-18
fluorodeoxyglucose (FDG) Positron Emission Tomography–Computed Tomography (PET …

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

B Kocak, EA Kus, O Kilickesmez - European Radiology, 2021 - Springer
In recent years, there has been a dramatic increase in research papers about machine
learning (ML) and artificial intelligence in radiology. With so many papers around, it is of …

More accurate and efficient segmentation of organs‐at‐risk in radiotherapy with convolutional neural networks cascades

K Men, H Geng, C Cheng, H Zhong, M Huang… - Medical …, 2019 - Wiley Online Library
Purpose Manual delineation of organs‐at‐risk (OAR s) in radiotherapy is both time‐
consuming and subjective. Automated and more accurate segmentation is of the utmost …

PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma

SP Haider, A Mahajan, T Zeevi, P Baumeister… - European journal of …, 2020 - Springer
Purpose To devise, validate, and externally test PET/CT radiomics signatures for human
papillomavirus (HPV) association in primary tumors and metastatic cervical lymph nodes of …

Toward high-throughput artificial intelligence-based segmentation in oncological PET imaging

F Yousefirizi, AK Jha, J Brosch-Lenz, B Saboury… - PET clinics, 2021 - pet.theclinics.com
An array of artificial intelligence (AI) techniques in the field of medical imaging has emerged
in the past decade for automated image segmentation. 1 Medical image segmentation seeks …

[HTML][HTML] Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks

WT Le, E Vorontsov, FP Romero, L Seddik… - Scientific Reports, 2022 - nature.com
In radiation oncology, predicting patient risk stratification allows specialization of therapy
intensification as well as selecting between systemic and regional treatments, all of which …

AI-based detection, classification and prediction/prognosis in medical imaging: towards radiophenomics

F Yousefirizi, P Decazes, A Amyar, S Ruan… - PET clinics, 2022 - pet.theclinics.com
The task of clinical interpretation of medical images starts with the scanning of the presented
image to detect the suspicious finding (“observation” in RadLex terminology (RID5) 1 which …

[HTML][HTML] Building reliable radiomic models using image perturbation

X Teng, J Zhang, A Zwanenburg, J Sun, Y Huang… - Scientific Reports, 2022 - nature.com
Radiomic model reliability is a central premise for its clinical translation. Presently, it is
assessed using test–retest or external data, which, unfortunately, is often scarce in reality …

Radiomics-based machine learning for outcome prediction in a multicenter phase II study of programmed death-ligand 1 inhibition immunotherapy for glioblastoma

E George, E Flagg, K Chang, HX Bai… - American Journal …, 2022 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in
glioblastoma is challenging due to overlap in the appearance of treatment-related changes …