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.

Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening.

The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules' (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the PET examination, and the ultimate outcome (benign/malignant), determined by patient follow-up or biopsy, was used to build the ML classifier.

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study.

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes.

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities.

Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers.

A Deep Learning Methodology for the Detection of Abnormal Parathyroid Glands via Scintigraphy with Tc-Sestamibi.

Background: Parathyroid proliferative disorder encompasses a wide spectrum of diseases, including parathyroid adenoma (PTA), parathyroid hyperplasia, and parathyroid carcinoma. Imaging modalities that deliver their results preoperatively help in the localisation of parathyroid glands (PGs) and assist in surgery. Artificial intelligence and, more specifically, image detection methods, can assist medical experts and reduce the workload in their everyday routine.

Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.

Purpose: This paper reviews recent applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging. Recent advances in Deep Learning (DL) and GANs catalysed the research of their applications in medical imaging modalities. As a result, several unique GAN topologies have emerged and been assessed in an experimental environment over the last two years.

Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques.

Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as well as doctor liberation from time-consuming procedures. The purpose of this study is the automatic and reliable characterization of SPNs in CT scans extracted from Positron Emission Tomography and Computer Tomography (PET/CT) system. To achieve the aforementioned task, Deep Learning with Convolutional Neural Networks (CNN) is applied.

Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data.

Purpose: Accurate detection and treatment of Coronary Artery Disease is mainly based on invasive Coronary Angiography, which could be avoided provided that a robust, non-invasive detection methodology emerged. Despite the progress of computational systems, this remains a challenging issue. The present research investigates Machine Learning and Deep Learning methods in competing with the medical experts' diagnostic yield.

Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation.

Objective: To investigate a deep learning technique, more specifically state-of-the-art convolutional neural networks (CNN), for automatic characterization of polar maps derived from myocardial perfusion imaging (MPI) studies for the diagnosis of coronary artery disease.

Subjects And Methods: Stress and rest polar maps corresponding to 216 patient cases from the database of the department of Nuclear Medicine of our institution were analyzed. Both attenuation-corrected (AC) and non-corrected (NAC) images were included.

¹¹¹In-DTPA⁰-octreotide (Octreoscan), ¹³¹I-MIBG and other agents for radionuclide therapy of NETs.

This paper is a critical review of the literature on NET radionuclide therapy with (111)In-DTPA(0)-octreotide (Octreoscan) and (131)I-MIBG, focusing on efficacy and toxicity. Some potential future applications and new candidate therapeutic agents are also mentioned. Octreoscan has been a pioneering agent for somatostatin receptor radionuclide therapy.

Diagnostic accuracy of ¹²³I-FP-CIT (DaTSCAN) in dementia with Lewy bodies: a meta-analysis of published studies.

Purpose: A systematic meta-analysis of published studies on the diagnostic accuracy of presynaptic dopaminergic imaging with ¹²³I-FP-CIT (DaTSCAN) in dementia with Lewy bodies (DLB).

Methods: We included (a) studies in which DaTSCAN was performed in cases of diagnostic uncertainty to differentiate between DLB and non-DLB dementia and (b) studies of patients with already established diagnoses of DLB, non-DLB dementia, or normalcy, against which the diagnostic accuracy of DaTSCAN was tested. We applied fixed-effects Mantel-Haenszel and hierarchical logistic regression models for meta-analysis of the diagnostic test's accuracy.

18F-FDG PET/CT and 123I-metaiodobenzylguanidine imaging in high-risk neuroblastoma: diagnostic comparison and survival analysis.

Unlabelled: The aim of our study was to evaluate prospectively the diagnostic performance and prognostic significance of (18)F-FDG PET/CT in comparison with (123)I-metaiodobenzylguanidine ((123)I-MIBG) imaging in patients with high-risk neuroblastoma.

Methods: Twenty-eight patients with refractory or relapsed high-risk neuroblastoma (16 male and 12 female patients; age range, 2-45 y; median age, 7.5 y) were simultaneously evaluated with (18)F-FDG PET/CT and (123)I-MIBG imaging before treatment with high-dose (131)I-MIBG.

The impact of non-discretionary factors on DEA and SFA technical efficiency differences.

The purpose of this study was to examine if factors of the external operating environment can explain differences in technical efficiency derived from Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). In a sample of 124 dialysis facilities, technical efficiency was compared according to ownership, region, years in operation and size. With second-stage Tobit regression, DEA and SFA efficiency was regressed against these environmental factors to determine their potential for predicting technical efficiency, as well as the efficiency differences between the two frontier methods.

Procedure guidelines for PET/CT tumour imaging with 68Ga-DOTA-conjugated peptides: 68Ga-DOTA-TOC, 68Ga-DOTA-NOC, 68Ga-DOTA-TATE.

The aim of these guidelines is to assist nuclear medicine physicians in recommending, performing, reporting and interpreting the results of somatostatin (SST) receptor PET/CT imaging using 68Ga-DOTA-conjugated peptides, analogues of octreotide, that bind to SST receptors. This imaging modality should not be regarded as the only approach to visualizing tumours expressing SST receptors or as excluding other imaging modalities useful for obtaining comparable results. The corresponding guidelines of 111In-pentetreotide scintigraphy imaging have been considered and partially integrated with this text.

Separating managerial inefficiency from influences of the operating environment: an application in dialysis.

In any production unit, the ability to achieve technical efficiency is influenced by characteristics of the external operating environment. This study uses the Greek dialysis sector to employ a previously reported frontier procedure to obtain a measure of managerial inefficiency that controls for exogenous features. The sample consisted of 124 dialysis facilities.

Assessment of left ventricular function at rest using rubidium-82 myocardial perfusion PET: comparison of four software algorithms with simultaneous 64-slice coronary CT angiography.

Objective: Left ventricular function is prognostically important. Our aim was to validate different algorithms' measurements with rubidium-82 PET, using computed tomography (CT) acquired simultaneously on hybrid imaging.

Methods: Fifty patients (33 men, 17 women, mean age 59 years SD 12) referred for coronary artery disease evaluation underwent rubidium-82 PET myocardial perfusion scintigraphy and 64-slice CT coronary angiography simultaneously on hybrid PET/CT.

Comparison of (68)Ga-DOTATATE and (18)F-fluorodeoxyglucose PET/CT in the detection of recurrent medullary thyroid carcinoma.

Purpose: This was a retrospective study to detect and map the extent of disease in recurrent medullary thyroid carcinoma (MTC) using the novel PET somatostatin analogue (68)Ga-DOTATATE and conventional (18)F-FDG positron emission tomography/computed tomography (PET/CT).

Methods: Eighteen patients (13 men, 5 women, median age: 54 years) who had previously been operated on for MTC and presented with biochemical (raised calcitonin levels) and/or imaging evidence of recurrence underwent both (68)Ga-DOTATATE and (18)F-FDG PET/CT within a maximum interval of 4 weeks (median interval of 1 week). (68)Ga-DOTATATE- and (18)F-FDG-avid lesions were recorded per patient as well as per region in six distinct regions: (1) thyroid bed-local recurrence, (2) cervical lymph nodes, (3) mediastinum, (4) lungs, (5) liver and (6) bones.

99mTc-depreotide in the evaluation of bone infection and inflammation.

Background And Aim: (99m)Tc-depreotide is a (99m)Tc-labelled somatostatin analogue, with high affinity for the 2, 3 and 5 subtypes of somatostatin receptors. These particular receptors are over-expressed on the surface of activated leucocytes, which mediate inflammatory response. Based on this property this study tried to investigate whether (99m)Tc-depreotide scintigraphy could be a useful complementary method in the investigation of bone infection and inflammation.