{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T20:07:38Z","timestamp":1780517258011,"version":"3.54.1"},"reference-count":65,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","pet.theclinics.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["PET Clinics"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.cpet.2026.02.005","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T20:07:39Z","timestamp":1775851659000},"page":"393-402","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Across the PSMA Theranostic Continuum in Prostate Cancer"],"prefix":"10.1016","volume":"21","author":[{"given":"Gowtham","family":"Murugesan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Moore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon","family":"Mancini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harshad R.","family":"Kulkarni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"22","key":"10.1016\/j.cpet.2026.02.005_bib1","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.3390\/cancers17223717","article-title":"PSMA theranostics in prostate cancer and beyond: current and future perspectives","volume":"17","author":"Sandhu","year":"2025","journal-title":"Cancers"},{"issue":"10231","key":"10.1016\/j.cpet.2026.02.005_bib2","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1016\/S0140-6736(20)30314-7","article-title":"Prostate-specific membrane antigen PET-CT in patients with high-risk prostate cancer before curative-intent surgery or radiotherapy (proPSMA): a prospective, randomised, multicentre study","volume":"395","author":"Hofman","year":"2020","journal-title":"Lancet"},{"issue":"Suppl 3","key":"10.1016\/j.cpet.2026.02.005_bib3","doi-asserted-by":"crossref","first-page":"97S","DOI":"10.2967\/jnumed.115.170167","article-title":"PSMA-based radioligand therapy for metastatic castration-resistant prostate cancer: the bad berka experience since 2013","volume":"57","author":"Kulkarni","year":"2016","journal-title":"J Nucl Med"},{"issue":"5","key":"10.1016\/j.cpet.2026.02.005_bib4","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.euf.2016.11.002","article-title":"68Ga-Labeled prostate-specific membrane antigen ligand positron emission tomography\/computed tomography for prostate cancer: a systematic review and meta-analysis","volume":"4","author":"von Eyben","year":"2018","journal-title":"Eur Urol Focus"},{"issue":"4","key":"10.1016\/j.cpet.2026.02.005_bib5","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.euo.2022.04.006","article-title":"Diagnostic performance of prostate-specific membrane antigen positron emission tomography\u2013targeted biopsy for detection of clinically significant prostate cancer: a systematic review and meta-analysis","volume":"5","author":"Kawada","year":"2022","journal-title":"Eur Urol Oncol"},{"issue":"6","key":"10.1016\/j.cpet.2026.02.005_bib6","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1016\/j.eururo.2016.06.021","article-title":"Sensitivity, specificity, and predictors of positive 68Ga\u2013Prostate-specific membrane antigen positron emission tomography in advanced prostate cancer: a systematic review and meta-analysis","volume":"70","author":"Perera","year":"2016","journal-title":"Eur Urol"},{"issue":"12","key":"10.1016\/j.cpet.2026.02.005_bib7","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1056\/NEJMoa2107322","article-title":"Lutetium-177\u2013PSMA-617 for metastatic castration-resistant prostate cancer","volume":"385","author":"Sartor","year":"2021","journal-title":"N Engl J Med"},{"issue":"7","key":"10.1016\/j.cpet.2026.02.005_bib8","doi-asserted-by":"crossref","first-page":"955","DOI":"10.2967\/jnumed.118.216820","article-title":"Clinical outcomes of 177Lu-PSMA radioligand therapy in earlier and later phases of metastatic castration-resistant prostate cancer grouped by previous taxane chemotherapy","volume":"60","author":"Barber","year":"2019","journal-title":"J Nucl Med"},{"issue":"3","key":"10.1016\/j.cpet.2026.02.005_bib9","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s11523-021-00801-w","article-title":"Real-World data analysis of efficacy and survival after Lutetium-177 labelled PSMA ligand therapy in metastatic castration-resistant prostate cancer","volume":"16","author":"Meyrick","year":"2021","journal-title":"Targeted Oncol"},{"issue":"8","key":"10.1016\/j.cpet.2026.02.005_bib10","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.2967\/jnumed.122.265259","article-title":"Safety and efficacy of [177Lu]-PSMA-I& T radioligand therapy in octogenarians with metastatic castration-resistant prostate cancer: report on 80 patients over the age of 80 years","volume":"64","author":"Tauber","year":"2023","journal-title":"J Nucl Med"},{"key":"10.1016\/j.cpet.2026.02.005_bib11","doi-asserted-by":"crossref","first-page":"S12","DOI":"10.1016\/j.urolonc.2024.01.065","article-title":"Early adverse events, emergency room visits and hospitalizations after PSMA based RLT: a retrospective analysis from real world theranostics practice","volume":"42","author":"Ahmed","year":"2024","journal-title":"Urol Oncol: Seminars and Original Investigations"},{"issue":"4","key":"10.1016\/j.cpet.2026.02.005_bib12","doi-asserted-by":"crossref","first-page":"286","DOI":"10.4274\/balkanmedj.galenos.2025.2025-4-69","article-title":"Generative artificial intelligence in prostate cancer imaging","volume":"42","author":"Haque","year":"2025","journal-title":"Balkan Med J"},{"issue":"6","key":"10.1016\/j.cpet.2026.02.005_bib13","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1007\/s00259-024-06594-0","article-title":"The accuracy and intra- and interobserver variability of PSMA PET\/CT for the local staging of primary prostate cancer","volume":"51","author":"Donswijk","year":"2024","journal-title":"European Journal of Nuclear Medicine and Molecular Imaging"},{"issue":"8","key":"10.1016\/j.cpet.2026.02.005_bib14","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.2967\/jnumed.122.265077","article-title":"A VISION substudy of reader agreement on 68Ga-PSMA-11 PET\/CT scan interpretation to determine patient eligibility for 177Lu-PSMA-617 radioligand therapy","volume":"64","author":"Kuo","year":"2023","journal-title":"J Nucl Med"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib15","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1038\/s41597-024-03977-8","article-title":"AI-Generated annotations dataset for diverse cancer radiology collections in NCI image data commons","volume":"11","author":"Murugesan","year":"2024","journal-title":"Sci Data"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib16","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1038\/s41597-025-05666-6","article-title":"AI generated annotations for breast, brain, liver, lungs, and prostate cancer collections in the National Cancer Institute Imaging Data Commons","volume":"12","author":"Murugesan","year":"2025","journal-title":"Sci Data"},{"issue":"2","key":"10.1016\/j.cpet.2026.02.005_bib17","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1002\/pro6.70014","article-title":"Deep learning (DL)-based advancements in prostate cancer imaging: artificial intelligence (AI)-based segmentation of 68Ga-PSMSA PET for tumor volume assessment","volume":"9","author":"Usmani","year":"2025","journal-title":"Precis Radiat Oncol"},{"issue":"11","key":"10.1016\/j.cpet.2026.02.005_bib18","doi-asserted-by":"crossref","first-page":"5386","DOI":"10.3390\/ijms26115386","article-title":"Transforming prostate cancer care: innovations in diagnosis, treatment, and future directions","volume":"26","author":"Vakili","year":"2025","journal-title":"Int J Mol Sci"},{"issue":"6","key":"10.1016\/j.cpet.2026.02.005_bib19","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1007\/s00259-024-07065-2","article-title":"PSMA PET\/CT based multimodal deep learning model for accurate prediction of pelvic lymph-node metastases in prostate cancer patients identified as candidates for extended pelvic lymph node dissection by preoperative nomograms","volume":"52","author":"Ma","year":"2025","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"5","key":"10.1016\/j.cpet.2026.02.005_bib20","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1007\/s11307-024-01943-9","article-title":"Comparison of the accuracy of a deep learning method for lesion detection in PET\/CT and PET\/MRI images","volume":"26","author":"Pang","year":"2024","journal-title":"Mol Imaging Biol"},{"issue":"3","key":"10.1016\/j.cpet.2026.02.005_bib21","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1007\/s00330-021-08245-6","article-title":"Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study","volume":"32","author":"Hinzpeter","year":"2021","journal-title":"Eur Radiol"},{"issue":"2","key":"10.1016\/j.cpet.2026.02.005_bib22","doi-asserted-by":"crossref","first-page":"233","DOI":"10.2967\/jnumed.120.261863","article-title":"aPROMISE: a novel automated PROMISE platform to standardize evaluation of tumor burden in 18F-DCFPyL images of veterans with prostate cancer","volume":"63","author":"Nickols","year":"2022","journal-title":"J Nucl Med"},{"key":"10.1016\/j.cpet.2026.02.005_bib23","article-title":"Teaching case advanced image analysis to select patients for prostate specific membrane antigen radioligand therapy and assess treatment response","volume":"10","author":"Floberg","year":"2025","journal-title":"Adv Radiat Oncol"},{"issue":"5","key":"10.1016\/j.cpet.2026.02.005_bib24","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1007\/s00259-023-06555-z","article-title":"A convolutional neural network\u2013based system for fully automatic segmentation of whole-body [68Ga]Ga-PSMA PET images in prostate cancer","volume":"51","author":"Jafari","year":"2023","journal-title":"Eur J Nucl Med Mol Imag"},{"key":"10.1016\/j.cpet.2026.02.005_bib25","author":"Murugesan"},{"issue":"5","key":"10.1016\/j.cpet.2026.02.005_bib26","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1007\/s10278-024-01104-y","article-title":"An automated deep learning-based framework for uptake segmentation and classification on PSMA PET\/CT imaging of patients with prostate cancer","volume":"37","author":"Li","year":"2024","journal-title":"J Imaging Inform Med"},{"key":"10.1016\/j.cpet.2026.02.005_bib27","article-title":"External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images","volume":"10","author":"Ghezzo","year":"2023","journal-title":"Front Med (Lausanne)"},{"issue":"4","key":"10.1016\/j.cpet.2026.02.005_bib28","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1007\/s00259-020-05040-1","article-title":"PSMA PET total tumor volume predicts outcome of patients with advanced prostate cancer receiving [177Lu]Lu-PSMA-617 radioligand therapy in a bicentric analysis","volume":"48","author":"Seifert","year":"2020","journal-title":"Eur J Nucl Med Mol Imag"},{"issue":"4","key":"10.1016\/j.cpet.2026.02.005_bib29","doi-asserted-by":"crossref","first-page":"605","DOI":"10.2967\/jnumed.122.264489","article-title":"Prognostic value of tumor volume assessment on PSMA PET after 177Lu-PSMA radioligand therapy evaluated by PSMA PET\/CT consensus statement and RECIP 1.0","volume":"64","author":"Kind","year":"2023","journal-title":"J Nucl Med"},{"issue":"5","key":"10.1016\/j.cpet.2026.02.005_bib30","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1007\/s00259-024-07060-7","article-title":"Staging of prostate cancer with ultra-fast PSMA-PET scans enhanced by AI","volume":"52","author":"Kersting","year":"2025","journal-title":"Eur J Nucl Med Mol Imag"},{"issue":"13","key":"10.1016\/j.cpet.2026.02.005_bib31","doi-asserted-by":"crossref","first-page":"4503","DOI":"10.1007\/s00259-022-05901-x","article-title":"Artificial intelligence guided enhancement of digital PET: scans as fast as CT?","volume":"49","author":"Hosch","year":"2022","journal-title":"Eur J Nucl Med Mol Imag"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib32","doi-asserted-by":"crossref","DOI":"10.1186\/s13550-023-00999-y","article-title":"Imaging quality of an artificial intelligence denoising algorithm: validation in 68Ga PSMA-11 PET for patients with biochemical recurrence of prostate cancer","volume":"13","author":"Margail","year":"2023","journal-title":"EJNMMI Res"},{"issue":"7","key":"10.1016\/j.cpet.2026.02.005_bib33","doi-asserted-by":"crossref","first-page":"234","DOI":"10.3390\/jimaging11070234","article-title":"Implantation of an artificial intelligence denoising algorithm using SubtlePETTM with various radiotracers: 18F-FDG, 68Ga PSMA-11 and 18F-FDOPA, impact on the technologist radiation doses","volume":"11","author":"Zhang-Yin","year":"2025","journal-title":"J Imaging"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib34","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s41747-022-00282-0","article-title":"Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics","volume":"6","author":"Liberini","year":"2022","journal-title":"Eur Radiol Exp"},{"issue":"11","key":"10.1016\/j.cpet.2026.02.005_bib35","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.3390\/jpm14111068","article-title":"Predicting response to [177Lu]Lu-PSMA therapy in mCRPC using machine learning","volume":"14","author":"Gong","year":"2024","journal-title":"J Personalized Med"},{"issue":"9","key":"10.1016\/j.cpet.2026.02.005_bib36","doi-asserted-by":"crossref","first-page":"818","DOI":"10.21037\/atm-20-6446","article-title":"Decision-support for treatment with 177Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET\/CT and clinical parameters","volume":"9","author":"Moazemi","year":"2021","journal-title":"Ann Transl Med"},{"issue":"18","key":"10.1016\/j.cpet.2026.02.005_bib37","doi-asserted-by":"crossref","first-page":"9971","DOI":"10.3390\/ijms22189971","article-title":"Prostate cancer radiogenomics\u2014from imaging to molecular characterization","volume":"22","author":"Ferro","year":"2021","journal-title":"Int J Mol Sci"},{"issue":"8","key":"10.1016\/j.cpet.2026.02.005_bib38","doi-asserted-by":"crossref","first-page":"2749","DOI":"10.1007\/s00259-025-07161-x","article-title":"The untapped potential of dosomics for theranostics: shaping the future of personalized medicine","volume":"52","author":"Filippi","year":"2025","journal-title":"Eur J Nucl Med Mol Imag"},{"issue":"6","key":"10.1016\/j.cpet.2026.02.005_bib39","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.7150\/thno.94788","article-title":"Theranostics and artificial intelligence: new frontiers in personalized medicine","volume":"14","author":"Bilgin","year":"2024","journal-title":"Theranostics"},{"issue":"11","key":"10.1016\/j.cpet.2026.02.005_bib40","doi-asserted-by":"crossref","first-page":"7222","DOI":"10.1002\/mp.16746","article-title":"A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in 177Lu-DOTATATE therapy","volume":"50","author":"Plachouris","year":"2023","journal-title":"Med Phys"},{"issue":"9","key":"10.1016\/j.cpet.2026.02.005_bib41","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.7150\/thno.93973","article-title":"Theranostic digital twins: concept, framework and roadmap towards personalized radiopharmaceutical therapies","volume":"14","author":"Abdollahi","year":"2024","journal-title":"Theranostics"},{"issue":"3","key":"10.1016\/j.cpet.2026.02.005_bib42","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.ijrobp.2025.05.014","article-title":"Machine learning-based dose prediction in [177Lu]Lu-PSMA-617 therapy by integrating biomarkers and radiomic features from [68Ga]Ga-PSMA-11 positron emission tomography\/computed tomography","volume":"123","author":"Yazdani","year":"2025","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"6","key":"10.1016\/j.cpet.2026.02.005_bib43","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.annonc.2023.03.001","article-title":"Next generation radiotheranostics promoting precision medicine","volume":"34","author":"Pomykala","year":"2023","journal-title":"Ann Oncol"},{"issue":"4","key":"10.1016\/j.cpet.2026.02.005_bib44","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J Nucl Med"},{"issue":"6","key":"10.1016\/j.cpet.2026.02.005_bib45","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1007\/s00259-020-05140-y","article-title":"Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET\/MRI","volume":"48","author":"Papp","year":"2021","journal-title":"Eur J Nucl Med Mol Imag"},{"key":"10.1016\/j.cpet.2026.02.005_bib46","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.1066926","article-title":"Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA","volume":"12","author":"Assadi","year":"2022","journal-title":"Front Oncol"},{"issue":"70","key":"10.1016\/j.cpet.2026.02.005_bib47","doi-asserted-by":"crossref","first-page":"33312","DOI":"10.18632\/oncotarget.26051","article-title":"Role of textural heterogeneity parameters in patient selection for 177Lu-PSMA therapy via response prediction","volume":"9","author":"Khurshid","year":"2018","journal-title":"Oncotarget"},{"issue":"15","key":"10.1016\/j.cpet.2026.02.005_bib48","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13153849","article-title":"Evaluation of 68Ga-PSMA-11 PET-MRI in patients with advanced prostate cancer receiving 177Lu-PSMA-617 therapy: a radiomics analysis","volume":"13","author":"Roll","year":"2021","journal-title":"Cancers (Basel)"},{"issue":"19","key":"10.1016\/j.cpet.2026.02.005_bib49","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.3390\/cancers16193369","article-title":"Prostate-specific membrane antigen-positron emission tomography-guided radiomics and machine learning in prostate carcinoma","volume":"16","author":"Maes","year":"2024","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.cpet.2026.02.005_bib50","doi-asserted-by":"crossref","DOI":"10.3389\/fnume.2023.1234853","article-title":"Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer","volume":"3","author":"Gutsche","year":"2023","journal-title":"Front Nucl Med"},{"key":"10.1016\/j.cpet.2026.02.005_bib51","doi-asserted-by":"crossref","DOI":"10.2967\/jnumed.125.270916","article-title":"Linking baseline PSMA PET\u2013derived parameters to toxicity, adverse events, pain, and quality of life in patients treated with [177Lu]Lu-PSMA-617: a single-center retrospective study","author":"Murthy","year":"2025","journal-title":"J Nucl Med"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib52","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13550-018-0408-2","article-title":"Impact of external cooling with icepacks on 68Ga-PSMA uptake in salivary glands","volume":"8","author":"van Kalmthout","year":"2018","journal-title":"EJNMMI Research"},{"issue":"9","key":"10.1016\/j.cpet.2026.02.005_bib53","doi-asserted-by":"crossref","first-page":"e431","DOI":"10.1016\/S1470-2045(20)30323-5","article-title":"Molecular profiling of neuroendocrine tumours to predict response and toxicity to peptide receptor radionuclide therapy","volume":"21","author":"Bodei","year":"2020","journal-title":"Lancet Oncol"},{"issue":"6","key":"10.1016\/j.cpet.2026.02.005_bib54","first-page":"688","article-title":"Gene abnormalities and modulated gene expression associated with radionuclide treatment: towards predictive biomarkers of response","volume":"15","author":"Smith","year":"2024"},{"issue":"7","key":"10.1016\/j.cpet.2026.02.005_bib55","doi-asserted-by":"crossref","first-page":"884","DOI":"10.2967\/jnumed.120.254995","article-title":"Pharmacogenomics in radionuclide therapy: impact on response to theranostics","volume":"62","author":"Scott","year":"2021","journal-title":"J Nucl Med"},{"issue":"23","key":"10.1016\/j.cpet.2026.02.005_bib56","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.3390\/cancers17233747","article-title":"Redefining prostate cancer precision: radiogenomics, theragnostics, and AI-Driven biomarkers","volume":"17","author":"Dorado","year":"2025","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib57","doi-asserted-by":"crossref","DOI":"10.1186\/s41824-023-00182-5","article-title":"Accuracy of combined multi-parametric MRI and PSMA PET-CT in diagnosing localized prostate cancer: newer horizons for a biopsy-free pathway","volume":"7","author":"Sharma","year":"2023","journal-title":"Eur J Hybrid Imaging"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib58","article-title":"AI-Supported shared decision-making (AI-SDM): conceptual framework","volume":"4","author":"As\u2019ad","year":"2025","journal-title":"JMIR AI"},{"key":"10.1016\/j.cpet.2026.02.005_bib59","doi-asserted-by":"crossref","unstructured":"Karaferis D, Balaska D, Pollalis Y. Design and development of data-driven AI to reduce the discrepancies in healthcare EHR utilization. doi:10.71010\/AJCMR.2025-e184","DOI":"10.71010\/AJCMR.2025-e184"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib60","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-22514-4","article-title":"Artificial intelligence-based methods for fusion of electronic health records and imaging data","volume":"12","author":"Mohsen","year":"2022","journal-title":"Sci Rep"},{"key":"10.1016\/j.cpet.2026.02.005_bib61","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2022-070904","article-title":"Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: decide-AI","volume":"377","author":"Vasey","year":"2022","journal-title":"BMJ"},{"issue":"9","key":"10.1016\/j.cpet.2026.02.005_bib62","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1038\/s41591-020-1041-y","article-title":"Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist","volume":"26","author":"Norgeot","year":"2020","journal-title":"Nat Med"},{"issue":"1","key":"10.1016\/j.cpet.2026.02.005_bib63","article-title":"Interpretable machine learning models for survival prediction in prostate cancer bone metastases","volume":"15","author":"Zhang","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.cpet.2026.02.005_bib64","doi-asserted-by":"crossref","DOI":"10.3389\/fsurg.2022.862322","article-title":"Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility?","volume":"9","author":"Naik","year":"2022","journal-title":"Front Surg"},{"key":"10.1016\/j.cpet.2026.02.005_bib65","article-title":"Reducing misdiagnosis in AI-driven medical diagnostics: a multidimensional framework for technical, ethical, and policy solutions","volume":"12","author":"Li","year":"2025","journal-title":"Front Med (Lausanne)"}],"container-title":["PET Clinics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1556859826000234?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1556859826000234?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T19:40:06Z","timestamp":1780515606000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1556859826000234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":65,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["S1556859826000234"],"URL":"https:\/\/doi.org\/10.1016\/j.cpet.2026.02.005","relation":{},"ISSN":["1556-8598"],"issn-type":[{"value":"1556-8598","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence Across the PSMA Theranostic Continuum in Prostate Cancer","name":"articletitle","label":"Article Title"},{"value":"PET Clinics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cpet.2026.02.005","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}