Background/Objectives: Lung cancer is the leading cause of cancer-related mortality worldwide. Accurate radiotherapy (RT) planning alongside chemotherapy and immunotherapy is critical for improving treatment outcomes for inoperable non-metastatic cases. Conventional computed tomography (CT)-based planning may be inadequate for accurately identifying tumor margins and the location of nodal disease. We investigated whether ¹⁸F-labeled fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET-CT) imaging can assist in target volume delineation for primary, nodal, and metastatic disease in the RT planning and overall therapeutic planning of patients. Methods: In this single-center, prospective study, we recruited 34 patients with histologically confirmed locally advanced non-small-cell lung carcinoma (NSCLC). All patients underwent ¹⁸F-FDG PET-CT-based RT simulation. Two sequential RT plans were created by the same radiation oncologist: one based on CT alone and the other PET-CT. Planning target volumes (PTVs) and PET-CT-guided adjustments were analyzed to assess their impact. Standardized protocols for immobilization, imaging, target delineation, and dose prescription were applied. Results: A total of 34 patients (31 males and 3 females) were recruited in the study. ¹⁸F-FDG PET-CT detected distant metastases in 7/34 (20.6%) patients, altering the overall therapeutic plan in 4/34 (11.8%) and allowing radical RT in 3 of them who had oligometastatic disease (8.8%). It modified RT planning in 26/34 (76.5%) patients and clarified malignancy in atelectatic areas. Nodal involvement was identified in 3/34 patients (8.8%) and excluded in 3/34 cases, avoiding unnecessary nodal irradiation. Additional involved nodes were revealed in 12/34 (35.3%) patients, requiring dose escalation. Overall, changes to the tumor PTV were made in 23/30 (76.6%) and to the nodal PTV in 19/30 (63.3%) cases (p < 0.0001). Primary tumor and nodal PTVs increased in 20/34 (66.7%) and 13/34 (43.3%), respectively. Conclusions: ¹⁸F-FDG PET-CT significantly improves RT planning by more precisely defining tumor and nodal volumes, identifying undetected lesions, and guiding dose adaptation. Larger long-term studies are required to confirm potential locoregional control and survival improvements.

Background/Objectives: Precise staging and tumor delineation are essential for optimizing treatment and enhancing outcomes of radiotherapy (RT). While computed tomography (CT)-based RT remains standard, fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) offers improved detection of primary and nodal disease. This study investigates the role of PET/CT in RT planning of HNSCC. Methods: Fifty-one HNSCC patients underwent radical volumetric modulated arc RT with concurrent cisplatin chemotherapy in a prospective study. Two RT plans per patient were sequentially created by a single oncologist using CT-only and PET/CT data, respectively. Planning target volumes (PTVs) for primary and nodal regions were independently defined, and dose–volume histograms were analyzed and compared. Results: PET/CT significantly affected TNM staging, increasing the T-stage in 11.8% of patients and the N-stage in 33.3%. Distant metastases were found in 9.8% of patients, leading to a redefinition of the overall treatment policy. PET/CT-based planning improved primary tumor PTV coverage (PTV4) in 37.2% (19/51) of cases. The tumor areas excluded from the CT-based planning received an average of 85.6% of the prescribed PTV4 dose (range 18–93%), while in PET/CT planning, they received 95.4% (range 93–99%) (p < 0.0001). Nodal PTV areas requiring a booster dose (PTV2) were adjusted in 33.3% (17/51) of patients during PET-CT planning. These nodal areas received an average of 85.6% of the prescribed dose for PTV2 (range 18–93%) during CT planning, compared to 95.4% (range 93–99%) during PET/CT planning. There was no statistically significant difference in the dose received by organs at risk between CT- and PET/CT-RT planning. Conclusions: PET/CT improves target delineation for primary tumors and lymph nodes, also allowing for dose escalation in metabolically highly active lesions in patients with HNSCC. The method also reveals occult distant metastases in a subset of patients, enabling personalized treatment strategies.

Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated the efficiency of seven ML models to diagnose the different PCa grades, changing the input variables. Our studied sample comprised 214 men who underwent bpMRI in different imaging centers. Seven ML algorithms were compared using radiomic features extracted from T2-weighted (T2W) and diffusion-weighted (DWI) MRI, with and without the inclusion of Prostate-Specific Antigen (PSA) values. The performance of the models was evaluated using the receiver operating characteristic curve analysis. The models’ performance was strongly dependent on the input parameters. Radiomic features derived from T2WI and DWI, whether used independently or in combination, demonstrated limited clinical utility, with AUC values ranging from 0.703 to 0.807. However, incorporating the PSA index significantly improved the models’ efficiency, regardless of lesion location or degree of malignancy, resulting in AUC values ranging from 0.784 to 1.00. There is evidence that ML methods, in combination with radiomic analysis, can contribute to solving differential diagnostic problems of prostate cancers. Also, optimization of the analysis method is critical, according to the results of our study.

Background/Objectives: This study aimed to investigate the prognostic value of advanced techniques of magnetic resonance imaging (MRI) biochemical recurrence (BCR) after radiotherapy in patients with prostate cancer (PCa). Methods: A comprehensive literature review was conducted to evaluate the role of MRI in detecting BCR of PCa patients after external beam radiation therapy. Results: National guidelines do not recommend imaging techniques in clinical follow-up PCa. However, in 2021, the European Association of Urogenital Radiology (ESUR), the European Association of Urological Imaging (ESUI), and the PI-RADS Steering Committee introduced the Prostate Imaging for Recurrence Reporting (PI-RR) system. PI-RR incorporates the MRI biomarkers in the post-treatment process. In the last decade, a growing number of clinical researchers have investigated the role of various MRI techniques in BCR. Conclusions: The integration of advanced MRI technologies into clinical routine marks the beginning of a new era of BCR with accuracy.

Objective: Butyrylcholinesterase (BChE) is an enzyme that shares similarities with acetylcholinesterase (AChE) but has been less studied due to its uncertain physiological role. Initially regarded as an ‘orphan enzyme,’ recent studies suggest that BChE plays a significant role in inflammation, primarily through its involvement in the cholinergic anti-inflammatory pathway. This enzyme, primarily produced by the liver, is found in several tissues and is known to hydrolyze acetylcholine, a neurotransmitter that modulates immune responses. BChE levels are influenced by various diseases, such as diabetes, cardiovascular diseases, and neurological disorders, which are associated with inflammation. This study reviews the costs of inflammatory biomarkers worldwide and investigates the association between BChE and inflammation. Data from multiple countries reveal varying costs for biomarkers like C-reactive protein (CRP), procalcitonin (PCT), and BChE, with BChE being a more cost-effective option in certain regions such as Greece and Armenia. Furthermore, BChE activity correlates inversely with the degree of systemic inflammation, and its levels may serve as a predictive marker for conditions such as surgical infections and anastomotic leaks. The findings underscore the potential of BChE as a useful and affordable biomarker for monitoring inflammation, particularly in settings where other biomarkers are more costly. However, further research is needed to validate its widespread clinical application.

Objective: Pain is a common and significant symptom in clinical practice, particularly among emergency and cancer patients. This article explores various pain scales and their clinical applications, alongside postoperative pain management in oncologic patients. A review of unidimensional pain scales, including the Numeric Rating Scale (NRS), Visual Analog Scale (VAS), Verbal Rating Scale (VRS), and Faces Pain Rating Scale (FPS), is presented. Additionally, the pathophysiology and pharmacological and non-pharmacological strategies for managing postoperative pain in cancer patients are discussed. Pain scales vary in sensitivity, ease of use, and clinical suitability. NRS is widely preferred for its simplicity and statistical applicability, whereas VAS offers higher sensitivity but practical limitations. Postoperative pain in oncologic patients is influenced by surgical trauma, psychological factors, and pre-existing pain conditions, with inadequate management leading to systemic complications and increased cancer recurrence risk. Effective management incorporates both non-opioid and opioid analgesics based on pain severity. Pain assessment tools and effective management strategies are critical for improving patient outcomes in clinical settings. Tailored approaches, particularly in oncologic postoperative care, can mitigate complications and enhance quality of life.