Review of artificial intelligence clinical applications in Nuclear Medicine

Panagiotidis, Emmanouil; Papachristou, Konstantinos; Makridou, Anna; Zoglopitou, Lydia-Aggeliki; Paschali, Anna; Kalathas, Theodoros; Chatzimarkou, Michael; Chatzipavlidou, Vasiliki

doi:10.1097/mnm.0000000000001786

Cited by 4 publications

(1 citation statement)

References 87 publications

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“…Artificial intelligence (AI) is being successfully used in nuclear medicine, focusing on several fields: neurology, oncology, and cardiology. AI is efficient for diagnosis and treatment planning for patients with PD, Alzheimer's disease, and cancer [124]. AI-based prediction models are being developed, playing an increasing role in screening, diagnosis, treatment selection, and rescue therapy decision making.…”

Section: Future Directionsmentioning

confidence: 99%

Association between Parkinson’s Disease and Cancer: New Findings and Possible Mediators

Surguchov,

Surguchev

2024

IJMS

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Epidemiological evidence points to an inverse association between Parkinson’s disease (PD) and almost all cancers except melanoma, for which this association is positive. The results of multiple studies have demonstrated that patients with PD are at reduced risk for the majority of neoplasms. Several potential biological explanations exist for the inverse relationship between cancer and PD. Recent results identified several PD-associated proteins and factors mediating cancer development and cancer-associated factors affecting PD. Accumulating data point to the role of genetic traits, members of the synuclein family, neurotrophic factors, the ubiquitin–proteasome system, circulating melatonin, and transcription factors as mediators. Here, we present recent data about shared pathogenetic factors and mediators that might be involved in the association between these two diseases. We discuss how these factors, individually or in combination, may be involved in pathology, serve as links between PD and cancer, and affect the prevalence of these disorders. Identification of these factors and investigation of their mechanisms of action would lead to the discovery of new targets for the treatment of both diseases.

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Section: Future Directionsmentioning

confidence: 99%

Association between Parkinson’s Disease and Cancer: New Findings and Possible Mediators

Surguchov,

Surguchev

2024

IJMS

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Towards improved diagnosis: radiomics and quantitative biomarkers in 18F-PSMA-1007 and 18F-fluorocholine PET/CT for prostate cancer recurrence

Panagiotidis,

Andreou,

Paschali

et al. 2024

Nuclear Medicine Communications

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Objective This study compared the radiomic features and quantitative biomarkers of 18F-PSMA-1007 [prostate-specific membrane antigen (PSMA)] and 18F-fluorocholine (FCH) PET/computed tomography (CT) in prostate cancer patients with biochemical recurrence (BCR) enrolled in the phase 3, prospective, multicenter BIO-CT-001 trial. Methods A total of 106 patients with BCR, who had undergone primary definitive treatment for prostate cancer, were recruited to this prospective study. All patients underwent one PSMA and one FCH PET/CT examination in randomized order within 10 days. They were followed up for a minimum of 6 months. Pathology, prostate-specific antigen (PSA), PSA doubling time, PSA velocity, and previous or ongoing treatment were analyzed. Using LifeX software, standardized uptake value (SUV) maximum, SUVmean, PSMA and choline total volume (PSMA-TV/FCH-TV), and total lesion PSMA and choline (TL-PSMA/TL-FCH) of all identified metastatic lesions in both tracers were calculated. Results Of the 286 lesions identified, the majority 140 (49%) were lymph node metastases, 118 (41.2%) were bone metastases and 28 lesions (9.8%) were locoregional recurrences of prostate cancer. The median SUVmax value was significantly higher for 18F-PSMA compared with FCH for all 286 lesions (8.26 vs. 4.99, respectively, P < 0.001). There were statistically significant differences in median SUVmean, TL-PSMA/FCH, and PSMA/FCH-TV as per table 2 between the two radiotracers (4.29 vs. 2.92, 1.97 vs. 1.53, and 7.31 vs. 4.37, respectively, P < 0.001). The correlation between SUVmean/SUVmax and PSA level was moderate, both for 18F-PSMA (r = 0.44, P < 0.001; r = 0.44, P < 0.001) and FCH (r = 0.35, P < 0.001; r = 0.41, P < 0.001). TL-PSMA/FCH demonstrated statistically significant positive correlations with both PSA level and PSA velocity for both 18F-PSMA (r = 0.56, P < 0.001; r = 0.57, P < 0.001) and FCH (r = 0.49, P < 0.001; r = 0.51, P < 0.001). While patients who received hormone therapy showed higher median SUVmax values for both radiotracers compared with those who did not, the difference was statistically significant only for 18F-PSMA (P < 0.05). Conclusion Our analysis using both radiomic features and quantitative biomarkers demonstrated the improved performance of 18F-PSMA-1007 compared with FCH in identifying metastatic lesions in prostate cancer patients with BCR.

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Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature

Czibor,

Csatlós,

Fábián

et al. 2024

Nuclear Medicine Communications

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Objectives To investigate the prognostic value of clinical, volumetric, and radiomics-based textural parameters in baseline [18F]FDG-PET/CT scans of diffuse large B-cell lymphoma (DLBCL) patients. Methods We retrospectively investigated baseline PET/CT scans and collected clinical data of fifty DLBCL patients. PET images were segmented semiautomatically to determine metabolic tumor volume (MTV), then the largest segmented lymphoma volume of interest (VOI) was used to extract first-, second-, and high-order textural features. A novel value, MTVrate was introduced as the quotient of the largest lesion’s volume and total body MTV. Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression. Results The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. A combined survival analysis including MTV and MTVrate identified a subgroup with particularly low PFS at 38%. In the machine learning-based model had an AUC of 0.83 and the highest relative importance was attributed to three textural features and both MTV and MTVrate as important predictors of PFS. Conclusion Individual evaluation of different biomarkers yielded only limited prognostic data, whereas a machine learning-based combined analysis had higher effectivity. MTVrate had the highest prognostic ability on individual analysis and, combined with MTV, it identified a patient group with particularly poor prognosis.

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Review of artificial intelligence clinical applications in Nuclear Medicine

Cited by 4 publications

References 87 publications

Association between Parkinson’s Disease and Cancer: New Findings and Possible Mediators

Association between Parkinson’s Disease and Cancer: New Findings and Possible Mediators

Towards improved diagnosis: radiomics and quantitative biomarkers in 18F-PSMA-1007 and 18F-fluorocholine PET/CT for prostate cancer recurrence

Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature

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