Preoperative assessment of lymph nodal metastases with [68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours.

PURPOSE: Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [68Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. METHODS: Seventy-two patients who performed preoperative [68Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [68Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney). RESULTS: LN metastases were detected in 29/72 patients at histology. [68Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*). CONCLUSION: In this study, [68Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.

Use of 18F-fluoro-2-deoxy-d-glucose (18F-FDG) PET/CT for lymph node assessment before radical cystectomy in bladder cancer patients.

OBJECTIVE: To assess the diagnostic performance of 18F-fluoro-2-deoxy-d-glucose (18F-FDG) positron emission tomograpy (PET)/computed tomography (CT) in nodal staging before radical cystectomy (RC) and pelvic lymph node dissection (PLND) for bladder cancer (BCa). MATERIALS AND METHODS: This analysis was based on a cohort of 199 BCa patients undergoing RC and bilateral PLND between 2015 and 2022. Neoadjuvant chemotherapy (NAC) or immunotherapy (NAI) was administered after oncological evaluation. All patients received preoperative 18F-FDG PET/CT to assess extravesical disease. Point estimates for true negative, false negative, false positive, true positive, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of conventional imaging and PET/CT were calculated. Subgroup analysis in patients receiving neoadjuvant treatment was performed. RESULTS: At preoperative evaluation, 30 patients (15.1%) had 48 suspicious nodal spots on 18F-FDG PET/CT. At RC and bilateral PLND, a total of 4871 lymph nodes (LNs) were removed with 237 node metastases corresponding to 126 different regions. Pathological node metastases were found in 17/30 (57%) vs 39/169 patients (23%) with suspicious vs negative preoperative 18F-FDG PET/CT, respectively (sensitivity = 0.30, specificity = 0.91, PPV = 0.57, NPV = 0.77, accuracy = 0.74). On per-region analysis including 1367 nodal regions, LN involvement was found in 19/48 (39%) vs 105/1319 (8%) suspicious vs negative regions at PET/CT, respectively (sensitivity = 0.15, specificity = 0.98, PPV = 0.40, NPV = 0.92, ACC = 0.90). Similar results were observed for patients receiving NAC (n = 44, 32.1%) and NAI (n = 93, 67.9% [per-patient: sensitivity = 0.36, specificity = 0.91, PPV = 0.59, NPV = 0.80, accuracy = 0.77; per-region: sensitivity = 0.12, specificity = 0.98, PPV = 0.32, NPV = 0.93, ACC = 0.91]). Study limitations include its retrospective design and limited patient numbers. CONCLUSIONS: In eight out of 10 patients with negative preoperative 18F-FDG PET/CT, pN0 disease was confirmed at final pathology. No differences were found based on NAC vs NAI treatment. These findings suggest that 18F-FDG PET/CT could play a role in the preoperative evaluation of nodal metastases in BCa patients, although its cost-effectiveness is uncertain.

Systematic Review and Metanalysis on the Role of Prostate-Specific Membrane Antigen Positron Emission Tomography/Magnetic Resonance Imaging for Intraprostatic Tumour Assessment.

The present systematic review and meta-analysis are focused on the diagnostic accuracy of PSMA PET/MRI in primary prostate cancer assessment. A literature search was conducted on the PubMed database using the terms "PSMA" AND "prostate cancer" or "prostate" AND "PET/MRI" or "PET MRI" or "PET-MRI" or "PET-MR" AND "primary" or "staging." Ten articles were eligible for analysis after applying the exclusion criteria. PET/MRI showed better diagnostic accuracy in detecting primary PCa compared to multiparametric (mp) MRI and PET alone. The pooled sensitivity and specificity of 68Ga-PSMA PET/MRI at the per-patient level were 0.976 (CI: 0.943-0.991) and 0.739 (CI: 0.437-0.912); respectively. PSMA PET/MRI has good sensitivity in detecting primary PCa, especially in patients with PIRADS 3 PCa.

Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible.

Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

Propelling Health Care into the Twenties.

The scope and potential of personalised health care are underappreciated and underrealised, often because of resistance to change. The consequence is that many inadequacies of health care in Europe persist unnecessarily, and many opportunities for improvement are neglected. This article identifies the principal challenges, outlines possible approaches to resolving them, and highlights the benefits that could result from greater adoption of personalised health care. It locates the discussion in the context of European policy, focusing particularly on the most recent and authoritative reviews of health care in the EU Member States, and on the newly acquired spirit of readiness and pragmatism among European officials to embrace change and innovative technologies in a new decade. It highlights the attention now being given by policymakers to incentives, innovation, and investment as levers to improve European citizens' prospects in a rapidly evolving world, and how these distinct and disruptive themes contribute to a renaissance in thinking about delivering optimal health care in Europe. It explores the chances offered to patients by specific initiatives in health domains such as cancer and antimicrobial resistance, and by innovative science, novel therapies, earlier diagnosis tools, and deeper understanding of health promotion and prevention. And it reflects on how health care providers could benefit from a shift towards better primary care and towards deploying health data more effectively, including the use of artificial intelligence, coupled with a move to a smoother organisational/regulatory structure and realigned professional responsibilities. The conclusion is that preparing Europe's health care systems for the inevitable strains of the coming years is both possible and necessary. A more courageous approach to embracing personalised health care could guarantee the sustainability of Europe's health care systems before rising demands and exponential costs overwhelm them - an exercise in future-proofing, in ensuring that they are equipped to withstand whatever lies ahead. A focus on the potential and implementation of personalised care would permit more efficient use of resources and deliver better quality health-preserving care.

Evaluation of diffusion-weighted MRI and (18F) fluorothymidine-PET biomarkers for early response assessment in patients with operable non-small cell lung cancer treated with neoadjuvant chemotherapy.

OBJECTIVE: To correlate changes in the apparent diffusion coefficient (ADC) from diffusion-weighted (DW)-MRI and standardised uptake value (SUV) from fluorothymidine (18FLT)-PET/CT with histopathological estimates of response in patients with non-small cell lung cancer (NSCLC) treated with neoadjuvant chemotherapy and track longitudinal changes in these biomarkers in a multicentre, multivendor setting. METHODS: 14 patients with operable NSCLC recruited to a prospective, multicentre imaging trial (EORTC-1217) were treated with platinum-based neoadjuvant chemotherapy. 13 patients had DW-MRI and FLT-PET/CT at baseline (10 had both), 12 were re-imaged at Day 14 (eight dual-modality) and nine after completing chemotherapy, immediately before surgery (six dual-modality). Surgical specimens (haematoxylin-eosin and Ki67 stained) estimated the percentage of residual viable tumour/necrosis and proliferation index. RESULTS: Despite the small numbers,significant findings were possible. ADCmedian increased (p < 0.001) and SUVmean decreased (p < 0.001) significantly between baseline and Day 14; changes between Day 14 and surgery were less marked. All responding tumours (>30% reduction in unidimensional measurement pre-surgery), showed an increase at Day 14 in ADC75th centile and reduction in total lesion proliferation (SUVmean x proliferative volume) greater than established measurement variability. Change in imaging biomarkers did not correlate with histological response (residual viable tumour, necrosis). CONCLUSION: Changes in ADC and FLT-SUV following neoadjuvant chemotherapy in NSCLC were measurable by Day 14 and preceded changes in unidimensional size but did not correlate with histopathological response. However, the magnitude of the changes and their utility in predicting (non-) response (tumour size/clinical outcome) remains to be established. ADVANCES IN KNOWLEDGE: During treatment, ADC increase precedes size reductions, but does not reflect histopathological necrosis.

Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT.

Aim: To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods: We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results: The algorithm, composed of two networks (a "feature extractor" and a "classifier"), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion: We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.

A risk management approach for imaging biomarker-driven clinical trials in oncology.

Imaging has steadily evolved in clinical cancer research as a result of improved conventional imaging methods and the innovation of new functional and molecular imaging techniques. Despite this evolution, the design and data quality derived from imaging within clinical trials are not ideal and gaps exist with paucity of optimised methods, constraints of trial operational support, and scarce resources. Difficulties associated with integrating imaging biomarkers into trials have been neglected compared with inclusion of tissue and blood biomarkers, largely because of inherent challenges in the complexity of imaging technologies, safety issues related to new imaging contrast media, standardisation of image acquisition across multivendor platforms, and various postprocessing options available with advanced software. Ignorance of these pitfalls directly affects the quality of the imaging read-out, leading to trial failure, particularly when imaging is a primary endpoint. Therefore, we propose a practical risk-based framework and recommendations for trials driven by imaging biomarkers, which allow identification of risks at trial initiation to better allocate resources and prioritise key tasks.

Long-Term Follow-up Results of the DANTE Trial, a Randomized Study of Lung Cancer Screening with Spiral Computed Tomography.

RATIONALE: Screening for lung cancer with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by 20% compared with screening with chest X-ray (CXR) in the National Lung Screening Trial, but uncertainty remains concerning the efficacy of LDCT screening in a community setting. OBJECTIVES: To explore the effect of LDCT screening on lung cancer mortality compared with no screening. Secondary endpoints included incidence, stage, and resectability rates. METHODS: Male smokers of 20+ pack-years, aged 60 to 74 years, underwent a baseline CXR and sputum cytology examination and received five screening rounds with LDCT or a yearly clinical review only in a randomized fashion. MEASUREMENTS AND MAIN RESULTS: A total of 1,264 subjects were enrolled in the LDCT arm and 1,186 in the control arm. Their median age was 64.0 years (interquartile range, 5), and median smoking exposure was 45.0 pack-years. The median follow-up was 8.35 years. One hundred four patients (8.23%) were diagnosed with lung cancer in the screening arm (66 by CT), 47 of whom (3.71%) had stage I disease; 72 control patients (6.07%) were diagnosed with lung cancer, with 16 (1.35%) being stage I cases. Lung cancer mortality was 543 per 100,000 person-years (95% confidence interval, 413-700) in the LDCT arm versus 544 per 100,000 person-years (95% CI, 410-709) in the control arm (hazard ratio, 0.993; 95% confidence interval, 0.688-1.433). CONCLUSIONS: Because of its limited statistical power, the results of the DANTE (Detection And screening of early lung cancer with Novel imaging TEchnology) trial do not allow us to make a definitive statement about the efficacy of LDCT screening. However, they underline the importance of obtaining additional data from randomized trials with intervention-free reference arms before the implementation of population screening.

Quantitative analyses at baseline and interim PET evaluation for response assessment and outcome definition in patients with malignant pleural mesothelioma.

PURPOSE: Quantitative analyses on FDG PET for response assessment are increasingly used in clinical studies, particularly with respect to tumours in which radiological assessment is challenging and complete metabolic response is rarely achieved after treatment. A typical example is malignant pleural mesothelioma (MPM), an aggressive tumour originating from mesothelial cells of the pleura. We present our results concerning the use of semiquantitative and quantitative parameters, evaluated at the baseline and interim PET examinations, for the prediction of treatment response and disease outcome in patients with MPM. METHODS: We retrospectively analysed data derived from 131 patients (88 men, 43 women; mean age 66 years) with MPM who were referred to our institution for treatment between May 2004 and July 2013. Patients were investigated using FDG PET at baseline and after two cycles of pemetrexed-based chemotherapy. Responses were determined using modified RECIST criteria based on the best CT response after treatment. Disease control rate, progression-free survival (PFS) and overall survival (OS) were calculated for the whole population and were correlated with semiquantitative and quantitative parameters evaluated at the baseline and interim PET examinations; these included SUVmax, total lesion glycolysis (TLG), percentage change in SUVmax (ΔSUVmax) and percentage change in TLG (ΔTLG). RESULTS: Disease control was achieved in 84.7 % of the patients, and median PFS and OS for the entire cohort were 7.2 and 14.3 months, respectively. The log-rank test showed a statistically significant difference in PFS between patients with radiological progression and those with partial response (PR) or stable disease (SD) (1.8 vs. 8.6 months, p < 0.001). Baseline SUVmax and TLG showed a statistically significant correlation with PFS and OS (p < 0.001). In the entire population, both ΔSUVmax and ΔTLG were correlated with disease control based on best CT response (p < 0.001). ΔSUVmax was significantly correlated with PFS in the entire population (p = 0.02) and with both PFS and OS in patients not undergoing talc pleurodesis (n = 65; p < 0.01 for PFS, p = 0.03 for OS), and in patients without pleurodesis presenting a SD and/or PR at CT after two cycles. CONCLUSION: These results confirm the role of FDG PET in the assessment of disease prognosis and treatment efficacy in MPM patients receiving first-line pemetrexed-based chemotherapy. In particular, metabolic response evaluated using ΔSUVmax can be used to predict outcome in MPM patients not undergoing talc pleurodesis who achieve SD and/or PR at the interim CT evaluation.

Assessment of tumor response in malignant pleural mesothelioma.

Most patients with malignant pleural mesothelioma (MPM) are candidates for chemotherapy during the course of their disease. Assessment of the response with conventional criteria based on computed tomography (CT) measurements is challenging, due to the circumferential and axial pattern of growth of MPM. Such difficulties hinder an accurate evaluation of clinical study results and make the clinical management of patients critical. Several radiological response systems have been proposed, but neither WHO criteria nor the more recent RECIST unidimensional criteria nor hybrid uni- and bidimensional criteria seem to apply to tumor measurement in this disease. Recently, modified RECIST criteria for MPM have been published. Although they are already being used in current clinical trials, they have been criticized based on the high grade of inter-observer variability and on theoretical studies of mesothelioma growth according to non-spherical models. Computer-assisted techniques for CT measurement are being developed. The use of FDG-PET for prediction of response and, more importantly, of survival outcomes of MPM patients is promising and warrants validation in large prospective series. New serum markers such as osteopontin and mesothelin-related proteins are under evaluation and in the future might play a role in assessing the response of MPM to treatment.

Utility of 18F-FDG PET and contrast-enhanced CT scan in the assessment of residual liver metastasis from colorectal cancer following adjuvant chemotherapy.

BACKGROUND: Neoadjuvant chemotherapy has been successfully used in the treatment of patients with colorectal liver metastases. The selection of patients for surgical resection after chemotherapy still poses a significant clinical challenge. (18)F-FDG PET is a useful tool in the assessment of liver metastases but the data regarding its sensitivity after chemotherapy is scarce. Our aim was to assess the value of this imaging modality in the selection of patients with colorectal liver metastasis for surgery following adjuvant chemotherapy. MATERIAL AND METHODS: We reviewed the diagnostic performances of (18)F-FDG PET and contrast-enhanced CT scan data from patients with colorectal liver metastases following treatment with chemotherapy. Nineteen patients (12 males, 7 females; median age 61 years; range 41-79) were evaluated. Chemotherapy regimens were: FOLFOX (14 patients), FOLFIRI (3 patients), 5-FU/FA (1 patient) and UFT-irinotecan-oxaliplatin (1 patient). Median time between end of chemotherapy and CT scan was 3.4 weeks, between end of chemotherapy and PET was 5.9 weeks and between end of chemotherapy and surgery was 9.9 weeks. All patients underwent surgery and had histopathological confirmation of liver lesions. Nine patients had segmentectomy, 2 patients had wedge resection, 5 patients had right hepatectomy and 3 patients had explorative laparotomy with liver biopsies. RESULTS: Data from all 19 patients, comprising 65 liver lesions, were confirmed by histo-pathology. Results on a per-lesion basis showed a sensitivity of 62% for (18)F-FDG PET and 70% for CT scan. A complete agreement between (18)F-FDG PET or CT scan and histology was documented in 5 and 3 patients, respectively. The sensitivity of (18)F-FDG PET was shown to increase for lesions larger than 1 cm (74% vs. 18%). CONCLUSIONS: These results suggest that (18)F-FDG PET and CT scan have sub-optimal sensitivity in the evaluation of colorectal liver lesions after neo-adjuvant chemotherapy, especially for lesions < 1 cm. The combined use of the two imaging techniques does not significantly increase the sensitivity of lesion detection.