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Purpose A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality. Methods Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination. Results The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths. Conclusions This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality.

Although systemic therapy represents the standard of care for polymetastatic kidney cancer, stereotactic body radiation therapy (SBRT) may play a relevant role in the oligometastatic setting. We conducted a multicenter study including oligometastatic kidney cancer treated with SBRT. We retrospectively analyzed 207 patients who underwent 245 SBRT treatments on 385 lesions, including 165 (42.9%) oligorecurrent (OR) and 220 (57.1%) oligoprogressive (OP) lesions. Most common sites were lung (30.9%) for OR group, and bone (32.7%) for OP group. Among 78 (31.8%) patients receiving concomitant systemic therapy, sunitinib (61.5%) and pazopanib (15.4%) were the most common for OR patients, while sunitinib (49.2%) and nivolumab (20.0%) for OP patients. End points were local control (LC), progression free survival (PFS), overall survival (OS), time to next systemic therapy (TTNS) and toxicity. Median follow-up was 18.6 months. 1, 2 and 3-year LC rates were 89.4%, 80.1% and 76.6% in OR patients, and 82.7%, 76.9% and 64.3% in those with OP, respectively. LC for OP group was influenced by clear cell histology (p = 0.000), total number of lesions (p = 0.004), systemic therapy during SBRT (p = 0.012), and SBRT dose (p = 0.012). Median PFS was 37.9 months. 1, 2- and 3-year OS was 92.7%, 86.4% and 81.8%, respectively. Median TTNS was 15.8 months for OR patients, and 13.9 months for OP patients. No grade 3 or higher toxicities were reported for both groups. SBRT may be considered an effective safe option in the multidisciplinary management of both OR and OP metastases from kidney cancer.

Objectives The aims of the study were (i) to examine the PCa detection rate of 18F-choline (FCH) PET/MRI and (ii) to assess the impact of PET/MRI findings in patients with PCa who develop OMD using PSA response as a biomarker. Methods We retrospectively analyzed a cohort of 103 patients undergoing FCH PET/MRI for biochemical recurrence of PCa. The inclusion criteria were (1) previous radical prostatectomy (RP) with or without adjuvant radiotherapy (RT); (2) PSA levels available at the time of PET; (3) OMD, defined as a maximum of 5 lesions on PET/MRI; and (4) follow-up data available for at least 6 months after PET. All images were reviewed by two nuclear medicine physicians and interpreted with the support of two radiologists. Results Seventy patients were eligible for the study: 52 patients had a positive FCH PET/MRI and 18 had a negative scan. The overall PCa detection rates for MRI, PET, and PET/MRI were 65.7%, 37.1%, and 74.3%, respectively. Thirty-five patients were treated with radiotherapy (RT), 16 received hormonal therapy (HT), 3 had a combined therapy (RT + HT), and 16 (23%) underwent PSA surveillance. At follow-up, PSA levels decreased in 51 patients (73%), most of whom had been treated with RT or RT + HT. Therapeutic management was guided by PET/MRI in 74% of patients, which performed better than MRI alone (68% of patients). Conclusion FCH PET/MRI has a higher detection rate than MRI or PET alone for PCa patients with OMD and PSA levels > 0.5 ng/mL, prompting a better choice of treatment.

Background/Objectives: This multicenter retrospective study aims to evaluate the role of Ablative Radiotherapy (RT) in patients with unresectable pleural mesothelioma (PM) who experienced radiological progression after at least one line of chemotherapy, with a maximum involvement of three pleural or extrapleural sites. Methods: Adult patients (≥18 years) with PM treated with stereotactic radiotherapy between 2011 and 2022, limited to a maximum of three pleural or extrapleural sites, were included in the analysis. Ablative RT was required to be administered with radical intent. Endpoints were time to further systemic therapy (TFST), local control (LC), progression-free survival (PFS), overall survival (OS), and acute and late radiotherapy-related toxicity. Results: A total of 56 patients were identified from six Italian and one Swiss radiotherapy center. Treatment was generally well tolerated. Ten patients experienced grade 1 or 2 acute toxicity, while four patients reported persistent chest pain, with one case reaching grade 3 as late toxicity. The median TFST was 18.6 months, with TFST rates of 61.7% and 46.4% at 12 and 24 months, respectively. The median OS was 37.63 months, with 1- and 2-year OS rates of 85.2% and 65.6%. Local control was favorable (79% at 1 year), but most patients experienced disease recurrence outside the SABR treatment volume. The median disease progression-free survival (DPFS) was 8.17 months, with 1- and 2-year DPFS rates of 36% and 19%, respectively. Smoking history correlated with OS and DPFS in univariate analysis, while statistical significance for OS was maintained in multivariate analysis. Additionally, nodal status and PTV volume were associated with OS. Conclusion: SABR is a safe and effective approach for the treatment of oligorecurrent/oligoprogressive PM. The time to further systemic therapy was extended up to 18 months. At two years, 10% of patients remained disease-free, and more than half were alive at three years, suggesting a potentially indolent biological behavior in oligometastatic PM.

Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.

Background: Pancoast tumors are a rare subset of lung cancers that require a multimodal approach (induction chemoradiotherapy and surgery), best performed in highly specialized centers. This study analyzes the outcomes and prognostic factors in patients treated at a high-volume center over an extended period. Methods: We retrospectively reviewed 43 patients who underwent surgery for Pancoast tumors, following induction treatment between 2005 and 2023. Survival was estimated using the Kaplan–Meier method, and a Cox proportional hazards model was applied to identify prognostic factors (significance level p = 0.05). Results: The median patient age was 63 years, with over 90% having a disease at stage III or higher. Induction chemoradiotherapy was administered to 79% of the patients, achieving a pathological complete response (PCR) in 23% of the patients. The median overall survival (OS) was 37 months, with 1–3 and 5-year OS rates of 71%, 52%, and 41%, respectively. The median disease-free survival (DFS) was 38 months, with 1-, 3-, and 5-year DFS rates of 72%, 62%, and 35%, respectively. A pathological complete response (PCR) and vertebral and/or vascular infiltration significantly influenced recurrence and mortality risk. Conclusions: Trimodal therapy still offers the best short- and long-term outcomes in patients with Pancoast tumors. Future strategies incorporating tyrosine kinase inhibitors and anti-PD1/PD-L1 may improve outcomes for patients by increasing PCR rates and improving disease control.

Background The RCC treatment landscape has evolved dramatically over the past decade. The purpose of this study is to present a real-world data estimation of RCC’s cost-of-illness for this tumour’s clinical pathway. Methods This investigation is a population-based cohort study using real-world data, which considers all RCC incident cases diagnosed in Local Unit 6 of the Province of Padua in 2016 and 2017 as registered by the Veneto Cancer Registry. Data on drug prescriptions, the use of medical devices, hospital admissions, and visits to outpatient clinics and emergency departments were collected by means of administrative databases. We evaluated the costs of all healthcare procedures performed in the 2 years of follow-up post-RCC diagnosis. The overall and annual average real-world costs per patient, both as a whole and by single item, were calculated and stratified by stage of disease at diagnosis. Results The analysis involved a population of 148 patients with a median age of 65.8 years, 66.22% of whom were male. Two years after diagnosis, the average total costs amounted to €21,429 per patient. There is a steady increment in costs with increasing stage at diagnosis, with a total amount of €41,494 spent 2 years after diagnosis for stage IV patients, which is 2.44 times higher than the expenditure for stage I patients (€17,037). In the first year, hospitalization appeared to be the most expensive item for both early and advanced disease. In the second year, however, outpatient procedures were the main cost driver in the earlier stages, whereas anticancer drugs accounted for the highest costs in the advanced stages. Conclusions This observational study provides real-world and valuable estimates of RCC’s cost-of-illness, which could enable policymakers to construct dynamic economic cost-effectiveness evaluation models based on real world costs’ evaluation.

Aim To study the feasibility of radiomic analysis of baseline [ 18 F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients. Material and methods Seventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PG whole : whole PG; PG 41% : prostate having standardized uptake value – SUV > 0.41*SUVmax; PG 2.5 : prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features. Results The median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG 2.5 and 0.4 discretization, for which the median test AUC was 0.78. Conclusion Radiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR. Clinical relevance statement The application of AI combined with radiomic analysis of [ 18 F]fluoromethylcholine PET/CT images has proven to be a promising tool to stratify patients with intermediate or high-risk PCa in order to predict biochemical recurrence and tailor the best treatment options. Key Points • Stratification of patients with intermediate and high-risk prostate cancer at risk of biochemical recurrence before initial treatment would help determine the optimal curative strategy. • Artificial intelligence combined with radiomic analysis of [ 18 F]fluorocholine PET/CT images allows prediction of biochemical recurrence, especially when radiomic features are complemented with patients’ clinical information (highest median AUC of 0.78). • Radiomics reinforces the information of conventional clinical parameters (i.e., Gleason score and initial prostate-specific antigen level) in predicting biochemical recurrence.

To study the feasibility of radiomic analysis of baseline [18F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients.AIMTo study the feasibility of radiomic analysis of baseline [18F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients.Seventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PGwhole: whole PG; PG41%: prostate having standardized uptake value - SUV > 0.41*SUVmax; PG2.5: prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features.MATERIAL AND METHODSSeventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PGwhole: whole PG; PG41%: prostate having standardized uptake value - SUV > 0.41*SUVmax; PG2.5: prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features.The median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG2.5 and 0.4 discretization, for which the median test AUC was 0.78.RESULTSThe median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG2.5 and 0.4 discretization, for which the median test AUC was 0.78.Radiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR.CONCLUSIONRadiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR.The application of AI combined with radiomic analysis of [18F]fluoromethylcholine PET/CT images has proven to be a promising tool to stratify patients with intermediate or high-risk PCa in order to predict biochemical recurrence and tailor the best treatment options.CLINICAL RELEVANCE STATEMENTThe application of AI combined with radiomic analysis of [18F]fluoromethylcholine PET/CT images has proven to be a promising tool to stratify patients with intermediate or high-risk PCa in order to predict biochemical recurrence and tailor the best treatment options.• Stratification of patients with intermediate and high-risk prostate cancer at risk of biochemical recurrence before initial treatment would help determine the optimal curative strategy. • Artificial intelligence combined with radiomic analysis of [18F]fluorocholine PET/CT images allows prediction of biochemical recurrence, especially when radiomic features are complemented with patients' clinical information (highest median AUC of 0.78). • Radiomics reinforces the information of conventional clinical parameters (i.e., Gleason score and initial prostate-specific antigen level) in predicting biochemical recurrence.KEY POINTS• Stratification of patients with intermediate and high-risk prostate cancer at risk of biochemical recurrence before initial treatment would help determine the optimal curative strategy. • Artificial intelligence combined with radiomic analysis of [18F]fluorocholine PET/CT images allows prediction of biochemical recurrence, especially when radiomic features are complemented with patients' clinical information (highest median AUC of 0.78). • Radiomics reinforces the information of conventional clinical parameters (i.e., Gleason score and initial prostate-specific antigen level) in predicting biochemical recurrence.