Prognostic value of pseudotime from texture parameters of FDG PET/CT in locally advanced rectal cancer with neoadjuvant concurrent chemoradiotherapy.

Texture analysis generates image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters correlate with tumor biology and clinical attributes, their types and implications can be complex. To overcome this limitation, pseudotime analysis was applied to texture parameters to estimate changes in individual sample characteristics, and the prognostic significance of the estimated pseudotime of primary tumors was evaluated.

Prognostic Significance of 18 F-FDG PET/CT Radiomics in Patients With Resectable Pancreatic Ductal Adenocarcinoma Undergoing Curative Surgery.

Purpose: This study aimed to investigate the prognostic significance of PET/CT radiomics to predict overall survival (OS) in patients with resectable pancreatic ductal adenocarcinoma (PDAC).

Methods: We enrolled 627 patients with resectable PDAC who underwent preoperative 18 F-FDG PET/CT and subsequent curative surgery. Radiomics analysis of the PET/CT images for the primary tumor was performed using the Chang-Gung Image Texture Analysis toolbox.

Metabolic bulk volume from FDG PET as an independent predictor of progression-free survival in follicular lymphoma.

Background: Total metabolic tumor volume (TMTV) in F-fluorodeoxyglucose (FDG) positron emission tomography (PET) predicts patient outcome in follicular lymphoma (FL); however, it requires laborious segmentation of all lesions. We investigated the prognostic value of the metabolic bulk volume (MBV) obtained from the single largest lesion.

Methods: Pretreatment FDG PET/computed tomography (CT) scans of 201 patients were analyzed for TMTV and MBV using a 41% maximum standardized uptake value (SUVmax) threshold.

Semi-quantitative FDG parameters predict survival in multiple myeloma patients without autologous stem cell transplantation.

Background: F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is useful in multiple myeloma (MM) for initial workup and treatment response evaluation. Herein, we evaluated the prognostic value of semi-quantitative FDG parameters for predicting the overall survival (OS) of MM patients with or without autologous stem cell transplantation (ASCT).

Methods: Study subjects comprised 227 MM patients who underwent baseline FDG PET/CT.

A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer.

Introduction: We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC).

Methods: Ninety-one CRC patients who underwent pretreatment FDG PET/computed tomography (CT) and palliative chemotherapy were retrospectively included. PET-based radiomics were extracted from the primary tumor on PET imaging using the software LIFEx.

A Machine Learning Approach Using [F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor.

Purpose: We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[F]fluoro-2-deoxy-D-glucose ([F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs).

Procedures: A total of 58 patients with PNETs who underwent pretherapeutic [F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method.

Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis.

F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a robust imaging modality used for staging multiple myeloma (MM) and assessing treatment responses. Herein, we extracted features from the FDG PET/CT images of MM patients using an artificial intelligence autoencoder algorithm that constructs a compressed representation of input data. We then evaluated the prognostic value of the image-feature clusters thus extracted.

A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer.

Background/aim: We explored the prediction of programmed cell death ligand 1 (PD-L1) expression level in non-small cell lung cancer using a machine learning approach with positron emission tomography/computed tomography (PET/CT)-based radiomics.

Patients And Methods: A total of 312 patients (189 adenocarcinomas, 123 squamous cell carcinomas) who underwent F-18 fluorodeoxyglucose PET/CT were retrospectively analysed. Imaging biomarkers with 46 CT and 48 PET radiomic features were extracted from segmented tumours on PET and CT images using the LIFEx package.

The Role of Preoperative F-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Retroperitoneal Sarcoma.

F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) was used to predict pathologic grades based on the maximum standardized uptake value (SUVmax) in soft tissue sarcoma and bone sarcoma. In retroperitoneal sarcoma (RPS), the effectiveness of PET was not well known. This study was designed to investigate the association of SUVmax with histopathologic grade and evaluate the usefulness of F-FDG PET/CT before operation.

Metabolism-Associated Gene Signatures for FDG Avidity on PET/CT and Prognostic Validation in Hepatocellular Carcinoma.

Introduction: The prognostic value of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in hepatocellular carcinoma (HCC) was established in previous reports. However, there is no evidence suggesting the prognostic value of transcriptomes associated with tumor FDG uptake in HCC. It was aimed to elucidate metabolic genes and functions associated with FDG uptake, followed by assessment of those prognostic value.

Predictive role of lymphoscintigraphy undergoing lymphovenous anastomosis in patients with lower extremity lymphedema: a preliminary study.

Background: We investigated whether preoperative lymphoscintigraphy could predict the treatment response of unilateral lymphovenous anastomosis (LVA) in patients with lower extremity lymphedema.

Materials And Methods: A total of 17 patients undergoing lymphoscintigraphy subsequent to LVA was included. As qualitative lymphoscintigraphic indicators, ilioinguinal lymph node uptake, main lymphatic vessel, collateral vessel, and four types of dermal backflow patterns (absent; distal only; proximal only; whole lower limb) were evaluated.

Role in staging and prognostic value of pretherapeutic F-18 FDG PET/CT in patients with gastric MALT lymphoma without high-grade transformation.

The purpose of this retrospective study was to investigate the role in staging and prognostic value of pretherapeutic fluorine-18-fluorodeoxyglucose (F-18 FDG) positron emission tomography (PET)/computed tomography (CT) in patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma without high-grade transformation (HT). We retrospectively reviewed 115 consecutive patients with histopathologically confirmed gastric MALT lymphoma without HT who underwent pretherapeutic F-18 FDG PET/CT. Kaplan-Meier and Cox proportional-hazards regression analyses were used to identify independent prognostic factors for disease free survival (DFS) among 13 clinical parameters and three PET parameters.

Performance Evaluation of a Deep Learning System for Differential Diagnosis of Lung Cancer With Conventional CT and FDG PET/CT Using Transfer Learning and Metadata.

Purpose: We aimed to evaluate the performance of a deep learning system for differential diagnosis of lung cancer with conventional CT and FDG PET/CT using transfer learning (TL) and metadata.

Methods: A total of 359 patients with a lung mass or nodule who underwent noncontrast chest CT and FDG PET/CT prior to treatment were enrolled retrospectively. All pulmonary lesions were classified by pathology (257 malignant, 102 benign).

Prognostic value of LI-RADS category on gadoxetic acid-enhanced MRI and 18F-FDG PET-CT in patients with primary liver carcinomas.

Objectives: To evaluate the postoperative prognostic value of the Liver Imaging Reporting and Data System (LI-RADS) category on gadoxetic acid-enhanced MRI and 18F-fluorodeoxyglucose PET-CT in patients with primary liver carcinomas (PLCs).

Methods: A total of 189 patients with chronic liver disease and surgically proven single PLC (42 intrahepatic cholangiocarcinomas and 21 combined hepatocellular-cholangiocarcinomas and 126 hepatocellular carcinomas [2:1 matching to non-HCC malignancies]) were retrospectively evaluated with gadoxetic acid-enhanced MRI and PET-CT. Two independent reviewers assigned an LI-RADS category for each observation.

Comparison of the prognostic values of F-fluorodeoxyglucose parameters from colon and non-colon sites of involvement in diffuse large B-cell lymphoma of the colon.

We examined the prognostic values of F-fluorodeoxyglucose (F-FDG) parameters from colon, non-colon, and total lesions in patients with diffuse large B-cell lymphoma (DLBCL) of the colon. Positron emission tomography/computed tomography (PET/CT) in 50 patients was retrospectively analyzed for maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG). During follow-up, 13 patients showed progression and 9 died from disease.

Development and Validation of a Deep Learning System for Diagnosing Glaucoma Using Optical Coherence Tomography.

This study aimed to develop and validate a deep learning system for diagnosing glaucoma using optical coherence tomography (OCT). A training set of 1822 eyes (332 control, 1490 glaucoma) with 7288 OCT images, an internal validation set of 425 eyes (104 control, 321 glaucoma) with 1700 images, and an external validation set of 355 eyes (108 control, 247 glaucoma) with 1420 images were included. Deviation and thickness maps of retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) analyses were used to develop the deep learning system for glaucoma diagnosis based on the visual geometry group deep convolutional neural network (VGG-19) model.

Correction to: Imaging phenotype using F-fluorodeoxyglucose positron emission tomography-based radiomics and genetic alterations of pancreatic ductal adenocarcinoma.

After publication of this article we received a request from Dr. Jong Kyun Lee to have his name removed from the author list as he felt he did not fully meet the authorship criteria. The original version of this article was inadvertently published with an incorrect inclusion period of study.

Imaging phenotype using F-fluorodeoxyglucose positron emission tomography-based radiomics and genetic alterations of pancreatic ductal adenocarcinoma.

Purpose: This study aimed to determine if major gene mutations including in KRAS, SMAD4, TP53, and CDKN2A were related to imaging phenotype using F-fluorodeoxyglucose (FDG) positron emission tomography (PET)-based radiomics in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods: Data on 48 PDAC patients with pretreatment FDG PET/CT who underwent genomic analysis of their tumor tissue were retrospectively analyzed. A total of 35 unique quantitative radiomic features were extracted from PET images, including imaging phenotypes such as pixel intensity, shape, and textural features.

Tumor SUVs on 18F-FDG PET/CT and Aggressive Pathological Features in Esophageal Squamous Cell Carcinoma.

Purpose: Considerable discrepancies are observed between clinical staging and pathological staging after surgical resection in patients with esophageal squamous cell carcinoma (ESCC). In this study, we examined the relationships between tumor SUVs on FDG PET/CT and aggressive pathological features in resected ESCC patients.

Methods: A total of 220 patients with surgically resected clinical stage I-II ESCC without neoadjuvant treatment were retrospectively analyzed.

A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer.

Purpose: We sought to distinguish lung adenocarcinoma (ADC) from squamous cell carcinoma using a machine-learning algorithm with PET-based radiomic features.

Methods: A total of 396 patients with 210 ADCs and 186 squamous cell carcinomas who underwent FDG PET/CT prior to treatment were retrospectively analyzed. Four clinical features (age, sex, tumor size, and smoking status) and 40 radiomic features were investigated in terms of lung ADC subtype prediction.

Diagnostic Performance of ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography/CT for Chronic Empyema-Associated Malignancy.

Objective: The purpose of this study was to evaluate the diagnostic performance of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) for chronic empyema-associated malignancy (CEAM).

Materials And Methods: We retrospectively reviewed the ¹⁸F-FDG PET/CT images of 33 patients with chronic empyema, and analyzed the following findings: 1) shape of the empyema cavity, 2) presence of fistula, 3) maximum standardized uptake value (SUV) of the empyema cavity, 4) uptake pattern of the empyema cavity, 5) presence of a protruding soft tissue mass within the empyema cavity, and 6) involvement of adjacent structures. Final diagnosis was determined based on histopathology or clinical follow-up for at least 6 months.

Diagnostic value of surveillance F-fluorodeoxyglucose PET/CT for detecting recurrent esophageal carcinoma after curative treatment.

Purpose: Esophageal carcinoma recurs within two years in approximately half of patients who receive curative treatment and is associated with poor survival. While F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is a reliable method of detecting recurrent esophageal carcinoma, in most previous studies FDG PET/CT scans were performed when recurrence was suspected. The aim of this study was to evaluate FDG PET/CT as a surveillance modality to detect recurrence of esophageal carcinoma after curative treatment where clinical indications of recurrent disease are absent.

Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning.

Purpose: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models.

Methods: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study.

Prognostic value of F-fluorodeoxyglucose positron emission tomography/computed tomography in patients with combined hepatocellular-cholangiocarcinoma.

Purpose: The prognostic value of pretreatment F-fluorodeoxyglucose positron emission tomography with computed tomography (FDG PET/CT) was assessed in patients with combined hepatocellular-cholangiocarcinoma (cHCC-CC).

Methods: A total of 46 patients with cHCC-CC who underwent FDG PET/CT before treatment were retrospectively analysed. Tumour FDG avidity was measured in terms of the tumour-to-normal liver standardized uptake value ratio (TLR) of the primary tumour on FDG PET/CT.