Search

Cancer therapy has advanced with molecularly targeted approaches and immunotherapy, yet chemotherapy remains essential for many aggressive cancers, including breast, lung, ovarian, pancreatic, bladder, sarcoma, and lymphomas. A major challenge is chemoresistance, in which cancer cells evade chemotherapy's cytotoxic effects. Overexpression of adenosine triphosphate-binding cassette transporters, especially P-glycoprotein, significantly contributes to this resistance. Thus, imaging biomarkers are urgently needed to detect P-glycoprotein overexpression in vivo, identify resistant cancer cell clones, and map their distribution and heterogeneity within tumors. This article reviews the applications of SPECT, PET, and optical imaging in addressing chemoresistance. It emphasizes the potential of these modalities to enhance cancer treatment by enabling early identification of resistant clones and improving therapeutic strategies. The article outlines key steps required for the integration of molecular imaging into clinical practice, aiming to overcome chemoresistance and optimize patient outcomes., (© 2025 by the Society of Nuclear Medicine and Molecular Imaging.)

Small renal masses (SRM), especially those under 7 cm pose significant diagnostic challenges when using conventional imaging (CT/MRI). PET/CT with [ 89 Zr]Zr-girentuximab offers a promising alternative in this setting by enabling molecular-level imaging. The ZIRCON trial, a phase 3 multicenter study, evaluated the diagnostic accuracy of [ 89 Zr]Zr-girentuximab PET/CT in detecting clear cell renal cell carcinoma (ccRCC) in SRM. A total of 300 patients with indeterminate renal masses received a single intravenous dose of [ 89 Zr]Zr-girentuximab, followed by PET/CT imaging 5 days post-injection. Generally, sensitivity and specificity for ccRCC detection were 85.5% and 87.0%, respectively. However, smaller masses (maximum diameter: ≤4 cm) demonstrated strong diagnostic performance, with mean sensitivity and specificity of 85% and 89.5%, respectively. PET positivity was observed exclusively in malignant lesions. Nevertheless, both benign and malignant non-ccRCC tumors are 'cold' at [ 89 Zr]Zr-girentuximab, thus differential diagnosis remains an unresolved issue. Despite its strengths, the trial highlights limitations: restricted imaging scope to the abdomen, lack of a cost-effectiveness analysis, concerns over radiation exposure given zirconium-89's 78.4-h half-life, and the daily scheduling of the examination. Only further studies and time will reveal whether the ZIRCON trial's findings will shine like a diamond or remain akin to zircon - brilliant but constrained in value.

Purpose: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [ 18 F]FDG PET/CT., Material and Methods: Primary tumor and the most significant lymph node metastasis were manually segmented in baseline [ 18 F]FDG PET/CT of 52 newly diagnosed BC patients. Clinical parameters, NAC and conventional semiquantitative PET parameters were collected. The standard of reference considered was surgical pCR after NAC (ypT0;ypN0). Eight-hundred-fifty-four radiomic features (RFts) were extracted from both PET and CT datasets, according to IBSI; robust RFTs were selected. The cohort was split in training (70%) and validation (30%) sets. Four ML Models (Clinical Model, CT Model, PET Model_T and PET Model_T + N) each one with 3 learners (Random Forest (RF), Neural Network and Stochastic Gradient Descendent) were trained and tested using RFts and clinical signatures. PET Models were built considering robust RFTs extracted from either primary tumor alone (PET Model_T) or also including the reference lymph node (PET Model_T + N)., Results: 72 pathological uptakes (52 primary BC and 20 lymph node metastasis) at [ 18 F]FDG PET/CT were segmented. pCR occurred in 44.2% cases. Twelve, 46 and 141 robust RFts were selected from CT Model, PET Model_T and PET Model_T + N, respectively. PET Models showed better performance than CT and Clinical Models. The best performances were obtained by the RF algorithm of the PET Model_T + N (AUC = 0.83;CA = 0.74;TP = 78%;TN = 72%)., Conclusion: ML models trained on PET/CT radiomic features extracted from primary BC and lymph node metastasis could concur in the prediction of pCR after NAC and improve BC management., Competing Interests: Declarations. Conflict of interest: The authors have no competing interests to declare that are relevant to the content of this article. Ethical approval: The local ethical committee approved the study (protocol number: 402/2024/Oss/AOUFE) and patients’ written informed consent was collected., (© 2025. The Author(s).)

Background/Objectives: Pulmonary arterial hypertension (PAH) is a complication of systemic sclerosis (SSc), and several screening algorithms have been proposed for the early detection of PAH in SSc. This study aimed to evaluate the predicting values of the DETECT algorithm for SSc-PAH screening in patients with SSc undergoing right heart catheterization (RHC) based on 2015 ESC/ERS echocardiographic criteria in a real-life setting. Methods: Patients fulfilling the 2013 ACR/EULAR classification criteria for SSc and with available data for PAH screening with the DETECT algorithm and the 2015 ESC/ERS echocardiographic criteria were retrospectively enrolled from January to June 2017 and then followed for 5 years. Baseline and annual clinical, laboratory, and instrumental data were collected. Results : A total of 33 out of 131 (25%) patients were selected based upon the ESC/ERS echocardiographic criteria, but 30 (23%) underwent RHC, while 51 (39%) patients with SSc were positive based on the DETECT algorithm. PAH diagnosis was confirmed in 28/30 cases (93.3%). The DETECT algorithm showed lower specificity and positive predictive value (PPV) ( p < 0.0001) but higher sensitivity and negative predictive value (NPV) ( p < 0.0001) than ESC/ERS criteria. Notably, patients with SSc with a negative DETECT screening at baseline had a low probability of developing PAH during a 5-year follow-up (OR 0.15, 95% CI 0.10-0.60- p < 0.0001). Conclusions: The DETECT algorithm has proven to be an easy, fast, and inexpensive tool for screening PAH in patients with SSc. Overall, a low probability of PAH using DETECT is highly predictive of a good prognosis.

The approval in 2017 by the European Medicines Agency (EMA) and in 2018 by the US Food and Drug Administration (FDA) of radioligand therapy (RLT) led to its wide application in therapeutic management of neuroendocrine neoplasms (NENs). However, the indications are currently limited to certain specific histotypes belonging to the broader NEN's family, mainly advanced well-differentiated gastro-entero-pancreatic NENs. As a consequence, several other tumors of the NEN spectrum that can potentially benefit, due to their biological characteristics, from RLT are still ineligible and can be considered "RLT-orphans". Among those, the subgroup of NENs originating from the sympathetic-adrenal-medullary (SAM) axis can be listed. This paper discusses the state of art and perspectives of the theragnostic applications in pheochromocytomas and paragangliomas, considering both the traditional theragnostic model - with radiolabelled metaiodobenzylguanidine (MIBG) - and the innovative one with radiolabeled somatostatin analogs (SSAs), that will hopefully become available for these patients in the near future., Competing Interests: Compliance with ethical standards. Conflict of interest: The authors declare no competing interests. Informed consents: informed consent was obtained by the patient in Fig. 1., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)