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This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detection and appropriate management of SPNs are critical to improving patient outcomes, necessitating efficient diagnostic methodologies. While CT and PET scans are pivotal in the diagnostic process, their interpretation remains prone to human error and delays in treatment implementation. This study proposes a machine-learning-based network to mitigate these concerns, integrating CT, PET, and manually extracted features in a multi-modal manner by integrating multiple image modalities and tabular features). CT and PET images are classified by a VGG19 network, while additional SPN features in combination with the outputs of VGG19 are processed by an XGBoost model to perform the ultimate diagnosis. The proposed methodology is evaluated using patient data from the Department of Nuclear Medicine of the University Hospital of Patras in Greece. We used 402 patient cases with human annotations to internally validate the model and 96 histopathological-confirmed cases for external evaluation. The model exhibited 97% agreement with the human readers and 85% diagnostic performance in the external set. It also identified the VGG19 predictions from CT and PET images, SUVmax, and diameter as key malignancy predictors. The study suggests that combining all available image modalities and SPN characteristics improves the agreement of the model with the human readers and the diagnostic efficiency. [ABSTRACT FROM AUTHOR]

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. We first trained a modified ResNet-18 DL model on CT data to predict HPV status. Next, radiomic features were extracted from manually segmented regions of interest near the oropharynx and used to train four ML models (K-Nearest Neighbors, logistic regression, decision tree, random forest) for the same purpose. Results: The CT-based model achieved the highest accuracy (90%) in classifying HPV status. Among the ML models, K-Nearest Neighbors performed best (80% accuracy). Weighted Ensemble methods combining the CT-based model with each ML model resulted in moderate accuracy improvements (70–90%). Conclusions: Our findings suggest that CT scans analyzed by DL models hold promise for non-invasive HPV detection in HNC. Radiomic features, while less accurate in this study, offer a complementary approach. Future research should explore larger datasets and investigate the potential of combining DL and radiomic techniques. [ABSTRACT FROM AUTHOR]

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. Two medical experts provided their malignancy likelihood scores, taking into account the patient's clinical condition and without prior knowledge of the true label of the SPN. Incorporating human assessments into ML model training improved diagnostic efficiency by approximately 3%, highlighting the synergistic role of human judgment alongside ML. Under the latter setup, the ML model had an accuracy score of 95.39% (CI 95%: 95.29–95.49%). While ML exhibited swings in probability scores, human readers excelled in discerning ambiguous cases. ML outperformed the best human reader in challenging instances, particularly in SPNs with ambiguous probability grades, showcasing its utility in diagnostic grey zones. The best human reader reached an accuracy of 80% in the grey zone, whilst ML exhibited 89%. The findings underline the collaborative potential of ML and human expertise in enhancing SPN characterization accuracy and confidence, especially in cases where diagnostic certainty is elusive. This study contributes to understanding how integrating ML and human judgement can optimize SPN diagnostic outcomes, ultimately advancing clinical decision-making in PET/CT screenings. [ABSTRACT FROM AUTHOR]

The pre-operative localisation of abnormal parathyroid glands (PG) in parathyroid scintigraphy is essential for suggesting treatment and assisting surgery. Human experts examine the scintigraphic image outputs. An assisting diagnostic framework for localisation reduces the workload of physicians and can serve educational purposes. Former studies from the authors suggested a successful deep learning model, but it produced many false positives. Between 2010 and 2020, 648 participants were enrolled in the Department of Nuclear Medicine of the University Hospital of Patras, Greece. An innovative modification of the well-known VGG19 network (ParaNet+) is proposed to classify scintigraphic images into normal and abnormal classes. The Grad-CAM++ algorithm is applied to localise the abnormal PGs. An external dataset of 100 patients imaged at the same department who underwent parathyroidectomy in 2021 and 2022 was used for evaluation. ParaNet+ agreed with the human readers, showing 0.9861 on a patient-level and 0.8831 on a PG-level basis under a 10-fold cross-validation on the training set of 648 participants. Regarding the external dataset, the experts identified 93 of 100 abnormal patient cases and 99 of 118 surgically excised abnormal PGs. The human-reader false-positive rate (FPR) was 10% on a PG basis. ParaNet+ identified 99/100 abnormal cases and 103/118 PGs, with an 11.2% FPR. The model achieved higher sensitivity on both patient and PG bases than the human reader (99.0% vs. 93% and 87.3% vs. 83.9%, respectively), with comparable FPRs. Deep learning can assist in detecting and localising abnormal PGs in scintigraphic scans of patients with primary hyperparathyroidism and can be adapted to the everyday routine. [ABSTRACT FROM AUTHOR]

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. Methods: The present study employs an innovative CNN topology called ParaNet, to analyse early MIBI, late MIBI, and TcO4 thyroid scan images simultaneously to perform first-level discrimination between patients with abnormal PGs (aPG) and patients with normal PGs (nPG). The study includes 632 parathyroid scans. Results: ParaNet exhibits a top performance, reaching an accuracy of 96.56% in distinguishing between aPG and nPG scans. Its sensitivity and specificity are 96.38% and 97.02%, respectively. PPV and NPV values are 98.76% and 91.57%, respectively. Conclusions: The proposed network is the first to introduce the automatic discrimination of PG and nPG scans acquired by scintigraphy with 99mTc-sestamibi (MIBI). This methodology could be applied to the everyday routine of medics for real-time evaluation or educational purposes. [ABSTRACT FROM AUTHOR]

Abstract: 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. The pooled sensitivity, specificity and diagnostic odds ratio (DOR) were estimated. Heterogeneity among studies was estimated by calculating the corresponding I 2 statistic. We tested for publication or other bias by creating the respective funnel plot. Results: Four studies, with a total of 419 subjects, were judged to be suitable for meta-analysis. According to hierarchical models, the estimated pooled sensitivity of DaTSCAN in differentiating DLB versus no DLB was 86.5% [95% Confidence Intervals (CI): 72–94.1%], the specificity was 93.6% (95% CI: 88.5–96.6%), and the DOR was 94.2 (95% CI: 25.7–345). The Mantel-Haenszel estimate of overall DOR was calculated at 48.95 (95% CI: 26.16–91.59). There was some heterogeneity among studies (I 2 = 45%), but this was not found statistically significant (χ 2 test for heterogeneity p value = 0.142). Funnel plot analysis showed no significant bias. Conclusion: Allowing for the small number of studies in the meta-analysis, our results showed high diagnostic accuracy of DaTSCAN in DLB diagnosis, especially in terms of specificity. [Copyright &y& Elsevier]

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 Ga-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 In-pentetreotide scintigraphy imaging have been considered and partially integrated with this text. The same has been done with the relevant and recent literature in this field and the final result has been discussed by distinguished experts. [ABSTRACT FROM AUTHOR]

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. It has achieved symptomatic responses and disease stabilization, but it is now outperformed by the corresponding β-emitter agents (177)Lu-DOTATATE and (90)Y-DOTATOC. (131)I-MIBG is the radionuclide therapy of choice for inoperable or metastatic phaeochromocytomas/paragangliomas, which avidly concentrate this tracer via the noradrenaline transporter. Symptomatic, biochemical and tumour morphological response rates of 50-89%, 45-74% and 27-47%, respectively, have been reported. (131)I-MIBG is a second-line radiopharmaceutical for treatment of enterochromaffin carcinoids, mainly offering the benefit of amelioration of hormone-induced symptoms. High specific activity, non-carrier-added (131)I-MIBG and meta-astato((211)At)-benzylguanidine (MABG) are tracers with potential for enhanced therapeutic efficacy, yet their integration into clinical practice awaits further exploration. Amongst other promising agents, radiolabelled exendin analogues show potential for imaging and possibly therapy of insulinomas, while preclinical studies are currently evaluating DOTA peptides targeting the CCK-2/gastrin receptors that are overexpressed by medullary thyroid carcinoma cells.