LMSST-GCN: Longitudinal MRI sub-structural texture guided graph convolution network for improved progression prediction of knee osteoarthritis.

Background And Objectives: Accurate prediction of progression in knee osteoarthritis (KOA) is significant for early personalized intervention. Previous methods commonly focused on quantifying features from a specific sub-structure imaged at baseline and resulted in limited performance. We proposed a longitudinal MRI sub-structural texture-guided graph convolution network (LMSST-GCN) for improved KOA progression prediction.

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally- advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This multicenter retrospective study included 1039 LA-NPC patients (779 male, 260 female, mean age 44 [standard deviation: 11]) diagnosed between April 2009 and December 2015. A radiomics- clinical prognostic model (Model RC) was developed using pre-and post-IC MRI and other clinical factors using graph convolutional neural networks (GCN).

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.

Background: Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.

Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer.

. To determine the optimal approach for identifying and mitigating batch effects in PET/CT radiomics features, and further improve the prognosis of patients with head and neck cancer (HNC), this study investigated the performance of three batch harmonization methods..

Multimodality radiomics analysis based on [F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis.

Objectives: To determine whether radiomics models developed from 2-deoxy-2-[F]fluoro-D-glucose ([F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients.

Methods: One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs.

Prognostic value of F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer.

Objectives: By comparing the prognostic performance of F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC.

Materials And Methods: A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model.

Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal F-FDG PET/CT.

Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on F-FDG PET/CT images.

Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered.

Functional-structural sub-region graph convolutional network (FSGCN): Application to the prognosis of head and neck cancer with PET/CT imaging.

Background And Objective: Accurate risk stratification is crucial for enabling personalized treatment for head and neck cancer (HNC). Current PET/CT image-based prognostic methods include radiomics analysis and convolutional neural network (CNN), while extracting radiomics or deep features in grid Euclidean space has inherent limitations for risk stratification. Here, we propose a functional-structural sub-region graph convolutional network (FSGCN) for accurate risk stratification of HNC.

Deep learning-based harmonization of CT reconstruction kernels towards improved clinical task performance.

Objectives: To develop a deep learning-based harmonization framework, assessing whether it can improve performance of radiomics models given different kernels in different clinical tasks and additionally generalize to mitigate the effects of new/unobserved kernels on radiomics features.

Methods: Patient data with 2 reconstruction kernels and phantom data with 22 reconstruction kernels were included. Eighty-five patients were studied for lymph node metastasis (LNM) prediction, and 164 patients for differential diagnosis between lung cancer (LC) and pulmonary tuberculosis (TB).

Prognostic generalization of multi-level CT-dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma.

Purpose: To investigate the prognostic performance of multi-level computed tomography (CT)-dose fusion dosiomics at the image-, matrix-, and feature-levels from the gross tumor volume (GTV) at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients.

Methods: Two hundred and nineteen NPC patients (175 vs. 44 for training vs.

Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer.

Purpose: This multi-center study aims to investigate the prognostic value of context-aware saliency-guided radiomics in F-FDG PET/CT images of head and neck cancer (HNC).

Methods: 806 HNC patients (training vs. validation vs.

Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma.

Objectives: To develop and validate the imbalanced data correction based PET/CT radiomics model for predicting lymph node metastasis (LNM) in clinical stage T1 lung adenocarcinoma (LUAD).

Methods: A total of 183 patients (148/35 non-metastasis/LNM) with pathologically confirmed LUAD were retrospectively included. The cohorts were divided into training vs.

Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model.

Objectives: This project aimed to construct an individualized PET/CT prognostic biomarker to accurately quantify the progression risk of patients with stage IIIC-IV epidermal growth factor receptor (EGFR)-mutated Non-small cell lung cancer (NSCLC) after first-line first and second generation EGFR- tyrosine kinase inhibitor (TKI) drug therapy and identify the first and second generation EGFR-TKI treatment-sensitive population.

Methods: A total of 250 patients with stage IIIC-IV EGFR-mutated NSCLC underwent first-line first and second generation EGFR-TKI drug therapy were included from two institutions (140 patients in training cohort; 60 patients in internal validation cohort, and 50 patients in external validation cohort). 1037 3D radiomics features were extracted to quantify the phenotypic characteristics of the tumor region in PET and CT images, respectively.

GapFill-Recon Net: A Cascade Network for simultaneously PET Gap Filling and Image Reconstruction.

PET image reconstruction from incomplete data, such as the gap between adjacent detector blocks generally introduces partial projection data loss, is an important and challenging problem in medical imaging. This work proposes an efficient convolutional neural network (CNN) framework, called GapFill-Recon Net, that jointly reconstructs PET images and their associated sinogram data. GapFill-Recon Net including two blocks: the Gap-Filling block first address the sinogram gap and the Image-Recon block maps the filled sinogram onto the final image directly.

Evaluation and optimization of radiomics features stability to respiratory motion in F-FDG 3D PET imaging.

Purpose: To evaluate the impact of respiratory motion on radiomics features in F-fluoro-2-deoxy-D-glucose three dimensional positron emission tomography ( F-FDG 3D PET) imaging and optimize feature stability by combining preprocessing configurations and aggregation strategies.

Methods: An in-house developed respiratory motion phantom was imaged in 3D PET scanner under nine respiratory patterns including one reference pattern. In total, 487 radiomics features were extracted for each respiratory pattern.

Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer.

Purpose: We aim to accurately differentiate between active pulmonary tuberculosis (TB) and lung cancer (LC) based on radiomics and semantic features as extracted from pre-treatment positron emission tomography/X-ray computed tomography (PET/CT) images.

Procedures: A total of 174 patients (77/97 pulmonary TB/LC as confirmed by pathology) were retrospectively selected, with 122 in the training cohort and 52 in the validation cohort. Four hundred eighty-seven radiomics features were initially extracted to quantify phenotypic characteristics of the lesion region in both PET and CT images.

Evaluation of the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with obstructive coronary artery disease.

Background: To investigate the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with suspected obstructive coronary artery disease (CAD).

Methods: Eighty-eight patients (53 and 35 applied for training and validation, respectively) with suspected obstructive CAD were referred to N-NH PET/CT myocardial perfusion imaging (MPI) and F-FDG PET/CT myocardial metabolic imaging (MMI) with available coronary angiography for analysis. One semi-quantitative indicator summed rest score (SRS) and five quantitative indicators, namely, perfusion defect extent (EXT), total perfusion deficit (TPD), myocardial blood flow (MBF), scar degree (SCR), and metabolism-perfusion mismatch (MIS), were extracted from the PET rest MPI and MMI scans.

Multi-Level Multi-Modality Fusion Radiomics: Application to PET and CT Imaging for Prognostication of Head and Neck Cancer.

To characterize intra-tumor heterogeneity comprehensively, we propose a multi-level fusion strategy to combine PET and CT information at the image-, matrix-and feature-levels towards improved prognosis. Specifically, we developed fusion radiomics in the context of 3 prognostic outcomes in a multi-center setting (4 centers) involving 296 head & neck cancer patients. Eight clinical parameters were first utilized to build a (1) clinical model.

Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma.

Purpose: This work aims to identify intratumoral habitats with distinct heterogeneity based on 2-deoxy-2-[F]fluro-D-glucose positron emission tomography (PET)/X-ray computed tomography (CT) imaging, and to develop a subregional radiomics approach to predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC).

Procedures: In total, 128 NPC patients (85 vs. 43 for primary vs.

Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.

Purpose: To identify optimal machine learning methods for radiomics-based differentiation of local recurrence versus inflammation from post-treatment nasopharyngeal positron emission tomography/X-ray computed tomography (PET/CT) images.

Procedures: Seventy-six nasopharyngeal carcinoma (NPC) patients were enrolled (41/35 local recurrence/inflammation as confirmed by pathology). Four hundred eighty-seven radiomics features were extracted from PET images for each patient.

Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer.

To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status. We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts.

[Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].

Objective: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.

Methods: The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using -test.