A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters.

BACKGROUND: In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment. METHODS: Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%). RESULTS: N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM. CONCLUSIONS: A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [Formula: see text]F]FDG PET/CT.

PURPOSE: PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients. METHODS: Automated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [Formula: see text]F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [Formula: see text]F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and lymph node (LN) metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively. RESULTS: In the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) ([Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in cross-validation and [Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in external testing). CONCLUSION: To the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.

Value of PET imaging for radiation therapy.

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.

A convolutional neural network for fully automated blood SUV determination to facilitate SUR computation in oncological FDG-PET.

PURPOSE: The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor's glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload, which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT. METHODS: Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. A total of 946 FDG PET/CT scans from several sites were used for network training (N = 366) and testing (N = 580). For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts or potential spillover from adjacent FDG-avid regions. Performance of the network was assessed using the fractional deviations of automatically and manually derived BSUVs in the test data. RESULTS: The trained U-Net yields BSUVs in close agreement with those obtained from manual delineation. Comparison of manually and automatically derived BSUVs shows excellent concordance: the mean relative BSUV difference was (mean ± SD) = (- 0.5 ± 2.2)% with a 95% confidence interval of [- 5.1,3.8]% and a total range of [- 10.0, 12.0]%. For four test cases, the derived ROIs were unusable (< 1 ml). CONCLUSION: CNNs are capable of performing robust automatic image-based BSUV determination. Integrating automatic BSUV derivation into PET data processing workflows will significantly facilitate SUR computation without increasing the workload in the clinical setting.

Impact of 68Ga-DOTATOC PET/MRI on robotic radiosurgery treatment planning in meningioma patients: first experiences in a single institution.

OBJECTIVEFor stereotactic radiosurgery (SRS) planning, precise contouring of tumor boundaries and organs at risk is of utmost importance. Correct interpretation of standard neuroimaging (i.e., CT and MRI) can be challenging after previous surgeries or in cases of skull base lesions with complex shapes. The aim of this study was to evaluate the impact of 68Ga-DOTATOC PET/MRI on treatment planning for image-guided SRS by CyberKnife.METHODSThe authors retrospectively identified 11 meningioma treatments in 10 patients who received a 68Ga-DOTATOC PET/MRI prior to SRS. The planning target volume (PTV) used for the patients' treatment was defined as the reference standard. This was contoured by a treating radiosurgeon (RS0) using fused planning CT and PET/MRI data sets. The same tumors were then contoured by another experienced radiosurgeon (RS1) and by a less-experienced radiosurgeon (RS2), both blinded to PET data sets. A comparison of target volumes with focus on volume-based metrics and distance to critical structures was performed. RS1 and RS2 also filled in a questionnaire analyzing the confidence level and the subjective need for the implementation of PET data sets for contouring.RESULTSAnalysis showed a subjective personal preference for PET/MRI in all cases for both radiosurgeons, particularly in proximity to critical structures. The analysis of the planning volumes per physician showed significantly smaller RS2-PTV in comparison to RS1-PTV and to RS0-PTV, whereas the median volumes were comparable between RS1-PTV and RS2-PTV (median: RS0: 4.3 cm3 [IQR 3.4-6.5 cm3] and RS1: 4.5 cm3 [IQR 2.7-6 cm3] vs RS2: 2.6 cm3 [IQR 2-5 cm3]; p = 0.003). This was also reflected in the best spatial congruency between the 2 experienced physicians (RS0 and RS1). The percentage of the left-out volume contoured by RS1 and RS2 compared to RS0 with PET/MRI demonstrated a relevant left-out-volume portion in both cases with greater extent for the less-experienced radiosurgeon (RS2) (RS1: 19.1% [IQR 8.5%-22%] vs RS2: 40.2% [IQR 34.2%-53%]). No significant differences were detected regarding investigated critical structures.CONCLUSIONSThis study demonstrated a relevant impact of PET/MRI on target volume delineation of meningiomas. The extent was highly dependent on the experience of the treating physician. This preliminary study supports the relevance of 68Ga-DOTATOC PET/MRI as a tool for radiosurgical treatment planning of meningiomas.

Ga-68-PSMA PET/CT in treatment-naïve patients with prostate cancer: Which clinical parameters and risk stratification systems best predict PSMA-positive metastases?

PURPOSE: To evaluate the accuracy of clinical parameters and established pre-treatment risk stratification systems for prostate cancer (PCa) in predicting PSMA-positive metastases in men undergoing Ga-68-PSMA PET/CT as initial staging examination. MATERIALS AND METHODS: A retrospective analysis in 108 consecutive treatment-naïve patients with biopsy-proven PCa undergoing Ga-68-PSMA PET/CT (median age, 72 years [range, 49-82 years]) was performed. Prediction of PSMA-positive metastases by serum PSA, clinical T stage (cT), ISUP group, percentage of positive biopsy cores, and derived risk scores (D'Amico risk classification system, Roach [RF], Yale formula [YF], and Briganti nomogram [BN]) was examined with ROC analysis. RESULTS: Any PSMA-positive metastases were found in 36 of 108 patients, including LN metastases in 28 patients, extrapelvic LN metastases in 15 patients, and organ metastases in 19 patients (bone, 19; lung, 1). AUCs for PSA, cT, ISUP, and percentage of positive biopsy cores regarding PSMA-positive metastases did not differ significantly (range, 0.6-0.8; each P > 0.05). D'Amico (AUC, 0.61-0.64) was inferior to RF (0.76-0.83), YF (0.81-0.86), and BN (0.73 to 0.88; each P < 0.05). Among the 89 high-risk patients (D'Amico), decision for or against PET imaging based on RF (cut-off, >18.0), YF (>10.8), or BN (>8.0) would have prevented PSMA PET/CT in 4 (5%), 15 (17%), or 18 patients (20%), respectively, while preserving a sensitivity ≥95% for PSMA-positive metastases. CONCLUSIONS: Clinical parameters and established risk stratification systems for PCa can predict Ga-68-PSMA PET-positive metastases in treatment-naïve patients. Especially YF and BN may improve identification of patients with the highest probability of metastatic disease detected by Ga-68-PSMA PET/CT.

Increased evidence for the prognostic value of FDG uptake on late-treatment PET in non-tumour-affected oesophagus in irradiated patients with oesophageal carcinoma.

PURPOSE: 18F-FDG uptake in irradiated non-tumour-affected oesophagus (NTO) on restaging PET is a potential surrogate for the measurement of radiation-induced inflammation. Radiation-induced inflammation itself has been shown to be of high prognostic relevance in patients undergoing preoperative radiochemotherapy (RCT) for locally advanced oesophageal cancer. We assessed the prognostic relevance of FDG uptake in the NTO in an independent cohort of patients treated with definitive RCT. METHODS: This retrospective evaluation included 72 patients with oesophageal squamous cell carcinoma treated with definitive RCT with curative intent. All patients underwent pretreatment and restaging FDG PET after receiving a radiation dose of 40-50 Gy. Standardized uptake values (SUVmax/SUVmean), metabolic tumour volume (MTV) and relative changes from pretreatment to restaging PET (∆SUVmax/∆SUVmean) were determined within the tumour and NTO. Univariate Cox regression with respect to overall survival (OS), local control (LC), distant metastases (DM) and treatment failure (TF) was performed. Independence of parameters was tested by multivariate Cox regression. RESULTS: ∆SUVmax NTO and MTV were prognostic factors for all investigated clinical endpoints (OS, LC, DM, TF). Inclusion of clinical and PET tumour parameters in multivariate analysis showed that ∆SUVmax NTO was an independent prognostic factor. Furthermore, multivariate analysis of ∆SUVmax NTO using previously published cut-off values from preoperatively treated patients revealed that ∆SUVmax NTO was independent prognostic factor for OS (HR = 1.88, p = 0.038), TF (HR = 2.11, p = 0.048) and DM (HR = 3.02, p = 0.047). CONCLUSION: NTO-related tracer uptake during the course of treatment in patients with oesophageal carcinoma was shown to be of high prognostic relevance. Thus, metabolically activity of NTO measured in terms of ∆SUVmax NTO is a potential candidate for future treatment individualization (i.e. organ preservation).

Pretherapeutic FDG-PET total metabolic tumor volume predicts response to induction therapy in pediatric Hodgkin's lymphoma.

BACKGROUND: Standardized treatment in pediatric patients with Hodgkin's lymphoma (HL) follows risk stratification by tumor stage, erythrocyte sedimentation rate and tumor bulk. We aimed to identify quantitative parameters from pretherapeutic FDG-PET to assist prediction of response to induction chemotherapy. METHODS: Retrospective analysis in 50 children with HL (f:18; m:32; median age, 14.8 [4-18] a) consecutively treated according to EuroNet-PHL-C1 (n = 42) or -C2 treatment protocol (n = 8). Total metabolic tumor volume (MTV) in pretherapeutic FDG-PET was defined using a semi-automated, background-adapted threshold. Metabolic (SUVmax, SUVmean, SUVpeak, total lesion glycolysis [MTV*SUVmean]) and heterogeneity parameters (asphericity [ASP], entropy, contrast, local homogeneity, energy, and cumulative SUV-volume histograms) were derived. Early response assessment (ERA) was performed after 2 cycles of induction chemotherapy according to treatment protocol and verified by reference rating. Prediction of inadequate response (IR) in ERA was based on ROC analysis separated by stage I/II (1 and 26 patients) and stage III/IV disease (7 and 16 patients) or treatment group/level (TG/TL) 1 to 3. RESULTS: IR was seen in 28/50 patients (TG/TL 1, 6/12 patients; TG/TL 2, 10/17; TG/TL 3, 12/21). Among all PET parameters, MTV best predicted IR; ASP was the best heterogeneity parameter. AUC of MTV was 0.84 (95%-confidence interval, 0.69-0.99) in stage I/II and 0.86 (0.7-1.0) in stage III/IV. In patients of TG/TL 1, AUC of MTV was 0.92 (0.74-1.0); in TG/TL 2 0.71 (0.44-0.99), and in TG/TL 3 0.85 (0.69-1.0). Patients with high vs. low MTV had IR in 86 vs. 0% in TG/TL 1, 80 vs. 29% in TG/TL 2, and 90 vs. 27% in TG/TL 3 (cut-off, > 80 ml, > 160 ml, > 410 ml). CONCLUSIONS: In this explorative study, high total MTV best predicted inadequate response to induction therapy in pediatric HL of all pretherapeutic FDG-PET parameters - in both low and high stages as well as the 3 different TG/TL. TRIAL REGISTRATION: Ethics committee number: EA2/151/16 (retrospectively registered).

Individualized risk assessment in neuroblastoma: does the tumoral metabolic activity on 123I-MIBG SPECT predict the outcome?

PURPOSE: Risk-adapted treatment in children with neuroblastoma (NB) is based on clinical and genetic factors. This study evaluated the metabolic tumour volume (MTV) and its asphericity (ASP) in pretherapeutic 123I-MIBG SPECT for individualized image-based prediction of outcome. METHODS: This retrospective study included 23 children (11 girls, 12 boys; median age 1.8 years, range 0.3-6.8 years) with newly diagnosed NB consecutively examined with pretherapeutic 123I-MIBG SPECT. Primary tumour MTV and ASP were defined using semiautomatic thresholds. Cox regression analysis, receiver operating characteristic analysis (cut-off determination) and Kaplan-Meier analysis with the log-rank test for event-free survival (EFS) were performed for ASP, MTV, laboratory parameters (including urinary homovanillic acid-to-creatinine ratio, HVA/C), and clinical (age, stage) and genetic factors. Predictive accuracy of the optimal multifactorial model was determined in terms of Harrell's C and likelihood ratio χ 2. RESULTS: Median follow-up was 36 months (range 7-107 months; eight patients showed disease progression/relapse, four patients died). The only significant predictors of EFS in the univariate Cox regression analysis were ASP (p = 0.029; hazard ratio, HR, 1.032 for a one unit increase), MTV (p = 0.038; HR 1.012) and MYCN amplification status (p = 0.047; HR 4.67). The mean EFS in patients with high ASP (>32.0%) and low ASP were 21 and 88 months, respectively (p = 0.013), and in those with high MTV (>46.7 ml) and low MTV were 22 and 87 months, respectively (p = 0.023). A combined risk model of either high ASP and high HVA/C or high MTV and high HVA/C best predicted EFS. CONCLUSIONS: In this exploratory study, pretherapeutic image-derived and laboratory markers of tumoral metabolic activity in NB (ASP, MTV, urinary HVA/C) allowed the identification of children with a high and low risk of progression/relapse under current therapy.

The asphericity of the metabolic tumour volume in NSCLC: correlation with histopathology and molecular markers.

PURPOSE: Asphericity (ASP) is a tumour shape descriptor based on the PET image. It quantitates the deviation from spherical of the shape of the metabolic tumour volume (MTV). In order to identify its biological correlates, we investigated the relationship between ASP and clinically relevant histopathological and molecular signatures in non-small-cell lung cancer (NSCLC). METHODS: The study included 83 consecutive patients (18 women, aged 66.4 ± 8.9 years) with newly diagnosed NSCLC in whom PET/CT with 18F-FDG had been performed prior to therapy. Primary tumour resection specimens and core biopsies were used for basic histopathology and determination of the Ki-67 proliferation index. EGFR status, VEGF, p53 and ALK expression were obtained in a subgroup of 44 patients. The FDG PET images of the primary tumours were delineated using an automatic algorithm based on adaptive thresholding taking into account local background. In addition to ASP, SUVmax, MTV and some further descriptors of shape and intratumour heterogeneity were assessed as semiquantitative PET measures. RESULTS: SUVmax, MTV and ASP were associated with pathological T stage (Kruskal-Wallis, p = 0.001, p < 0.0005 and p < 0.0005, respectively) and N stage (p = 0.017, p = 0.003 and p = 0.002, respectively). Only ASP was associated with M stage (p = 0.026). SUVmax, MTV and ASP were correlated with Ki-67 index (Spearman's rho = 0.326/p = 0.003, rho = 0.302/p = 0.006 and rho = 0.271/p = 0.015, respectively). The latter correlations were considerably stronger in adenocarcinomas than in squamous cell carcinomas. ASP, but not SUVmax or MTV, showed a tendency for a significant association with the extent of VEGF expression (p = 0.058). In multivariate Cox regression analysis, ASP (p < 0.0005) and the presence of distant metastases (p = 0.023) were significantly associated with progression-free survival. ASP (p = 0.006), the presence of distant metastases (p = 0.010), and Ki-67 index (p = 0.062) were significantly associated with overall survival. CONCLUSION: The ASP of primary NSCLCs on FDG PET images is associated with tumour dimensions and molecular markers of proliferation and angiogenesis.

Dual time point imaging for F18-FDG-PET/CT does not improve the accuracy of nodal staging in non-small cell lung cancer patients.

OBJECTIVES: To analyze the diagnostic performance of dual time point imaging (DTPI) for pre-therapeutic lymph node (LN) staging in non-small cell lung cancer (NSCLC). METHODS: This was a retrospective analysis of 47 patients with NSCLC who had undergone DTPI by PET (early + delayed) using F18-fluorodeoxyglucose (FDG). PET raw data were reconstructed iteratively (point spread function + time-of-flight). LN uptake in PET was assessed visually (four-step score) and semi-quantitatively (SUVmax, SUVmean, ratios LN/primary, LN/liver, and LN/mediastinal blood pool). DTPI analyses included retention indices (RIs), Δ-ratios and changes in visual score. Histology or cytology served as standards of reference. Accuracy was determined based on ROC analyses. RESULTS: Thirty-six of 155 LNs were malignant. DTPI accuracy was low for all measures (visual assessment, 24.5%; RI SUVmax, 68.4%; RI SUVmean, 65.8%; Δ-ratios, 63.9-76.1%) and significantly inferior to early PET. Accuracies of early (range, 86.5-92.9%) and delayed PET (range, 85.2-92.9%) were comparable. At early PET, accuracy of the visual score (92.9%) was similar or superior to semi-quantitative analyses (range, 86.5-92.3%). CONCLUSIONS: Using a modern PET/CT device and novel image reconstruction, neither additional delayed PET nor DTPI analyses improved the accuracy of PET-based LN staging. Dedicated visual assessment criteria performed very well. KEY POINTS: • DTPI did not improve accuracy of PET-based LN staging in NSCLC. • Analyzed SUV ratios were not superior to LN SUVmax or SUVmean. • A four-step visual score may allow highly accurate, standardized LN assessment.

Intrahepatic Activity Distribution in Radioembolization with Yttrium-90-Labeled Resin Microspheres Using the Body Surface Area Method--A Less than Perfect Model.

PURPOSE: To retrospectively assess the influence of the parameters of the body surface area (BSA) method in hepatic radioembolization using yttrium-90-labeled microspheres on the determination of the prescribed activity. MATERIALS AND METHODS: Data from 283 consecutive patients treated with radioembolization (BSA method) were included. For interindividual comparisons, activity concentrations (ACs; MBq/mL) were calculated for each liver. The impact of the BSA method parameters was assessed by analysis of variance and pairwise t test with Bonferroni-Holm correction. RESULTS: Prescribed activity was 1.01-2.71 GBq, with BSA, liver volume (LV), tumor burden, and the liver-lung shunt reduction factor (LLS RF) being significant contributing factors to the AC (all P < .0001, analysis of variance). BSA and LV correlated only moderately (ρ = 0.46, P < .0001). Compared with base activity defined by the BSA (median = 1.67 GBq; range, 1.20-2.32 GBq), the activity contribution of tumor burden was small (median = 150 MBq; range, 3-800 MBq). Resulting activities were reduced according to LLS RF by 20% in 12.4% and by 40% in 3.5% of patients. AC was significantly (up to 56%) lower in association with larger LV than in small LV (LV < 1,500 mL vs ≥ 2,500 mL, P < .0001). CONCLUSIONS: In the BSA model, BSA and LV showed only a moderate correlation, resulting in a significantly lower AC in patients with larger livers. Tumor burden percentage contributed little to the prescribed activity because the BSA model did not account for actual LVs and tumor volumes. These inaccuracies may potentially result in underdosage in patients with larger livers, especially if further LLS RF needs to be applied.

Quantitative assessment of the asphericity of pretherapeutic FDG uptake as an independent predictor of outcome in NSCLC.

BACKGROUND: The aim of the present study was to evaluate the predictive value of a novel quantitative measure for the spatial heterogeneity of FDG uptake, the asphericity (ASP) in patients with non-small cell lung cancer (NSCLC). METHODS: FDG-PET/CT had been performed in 60 patients (15 women, 45 men; median age, 65.5 years) with newly diagnosed NSCLC prior to therapy. The FDG-PET image of the primary tumor was segmented using the ROVER 3D segmentation tool based on thresholding at the volume-reproducing intensity threshold after subtraction of local background. ASP was defined as the relative deviation of the tumor's shape from a sphere. Univariate and multivariate Cox regression as well as Kaplan-Meier (KM) analysis and log-rank test with respect to overall (OAS) and progression-free survival (PFS) were performed for clinical variables, SUVmax/mean, metabolically active tumor volume (MTV), total lesion glycolysis (TLG), ASP and "solidity", another measure of shape irregularity. RESULTS: ASP, solidity and "primary surgical treatment" were significant independent predictors of PFS in multivariate Cox regression with binarized parameters (HR, 3.66; p<0.001, HR, 2.11; p=0.05 and HR, 2.09; p=0.05), ASP and "primary surgical treatment" of OAS (HR, 3.19; p=0.02 and HR, 3.78; p=0.01, respectively). None of the other semi-quantitative PET parameters showed significant predictive value with respect to OAS or PFS. Kaplan-Meier analysis revealed a probability of 2-year PFS of 52% in patients with low ASP compared to 12% in patients with high ASP (p<0.001). Furthermore, it showed a higher OAS rate in the case of low versus high ASP (1-year-OAS, 91% vs. 67%: p=0.02). CONCLUSIONS: The novel parameter asphericity of pretherapeutic FDG uptake seems to provide better prognostic value for PFS and OAS in NCSLC compared to SUV, metabolic tumor volume, total lesion glycolysis and solidity.

Reevaluating the relevance of 18F-FDG PET findings for diagnosis of neurosarcoidosis: a case series.

OBJECTIVE: The diagnosis of neurosarcoidosis (NS) remains challenging due to the difficulty to obtain central nervous system (CNS) biopsies. Various diagnostic parameters are considered for the definition of possible, probable and definite NS. Magnetic resonance imaging (MRI) is the imaging gold standard and considered in diagnostic criteria. Fluorodeoxyglucose positron emission (18F-FDG PET) is sometimes performed additionally to identify possible systemic biopsy targets. However, at present, its findings are not incorporated into the diagnostic criteria for neurosarcoidosis (NS). METHODS: We conducted a single center retrospective search for the period 2020-2022, for patients with neurological symptoms in a diagnostic context of suspected NS who underwent MRI and additional 18F-FDG PET scans to identify potential hypermetabolism in the CNS and biopsy targets. RESULTS: We identified three cases of NS, where Gadolinium-enhanced MRI scans did not show abnormalities while 18F-FDG PET revealed hypermetabolic lesions in areas of the CNS. Additional MRI scans were still inconclusive for structural changes. We diagnosed a "probable" NS in all cases with histopathological confirmation of systemic sarcoidosis which led to an intensified therapy regime. DISCUSSION: 18F-FDG PET is an early indicator for metabolic changes. It appears to be a useful add-on to improve accuracy of diagnostic criteria in suspected NS without MRI findings.

Keeping Up With ChatGPT: Evaluating Its Recognition and Interpretation of Nuclear Medicine Images.

PURPOSE: The latest iteration of GPT4 (generative pretrained transformer) is a large multimodal model that can integrate both text and image input, but its performance with medical images has not been systematically evaluated. We studied whether ChatGPT with GPT-4V(ision) can recognize images from common nuclear medicine examinations and interpret them. PATIENTS AND METHODS: Fifteen representative images (scintigraphy, 11; PET, 4) were submitted to ChatGPT with GPT-4V(ision), both in its Default and "Advanced Data Analysis (beta)" version. ChatGPT was asked to name the type of examination and tracer, explain the findings and whether there are abnormalities. ChatGPT should also mark anatomical structures or pathological findings. The appropriateness of the responses was rated by 3 nuclear medicine physicians. RESULTS: The Default version identified the examination and the tracer correctly in the majority of the 15 cases (60% or 53%) and gave an "appropriate" description of the findings or abnormalities in 47% or 33% of cases, respectively. The Default version cannot manipulate images. "Advanced Data Analysis (beta)" failed in all tasks in >90% of cases. A "major" or "incompatible" inconsistency between 3 trials of the same prompt was observed in 73% (Default version) or 87% of cases ("Advanced Data Analysis (beta)" version). CONCLUSIONS: Although GPT-4V(ision) demonstrates preliminary capabilities in analyzing nuclear medicine images, it exhibits significant limitations, particularly in its reliability (ie, correctness, predictability, and consistency).

Quantification in (68)Ga-DOTA(0)-Phe(1)-Tyr(3)-octreotide positron emission tomography/computed tomography: can we be impartial about partial volume effects?

AIM: In combined positron emission tomography/computed tomography (PET/CT) of neuroendocrine neoplasms using (68)Ga-DOTA(0)-Phe(1)-Tyr(3)-octreotide ((68)Ga-DOTATOC), partial volume effects (PVEs) may occur in smaller lesions. This study determined the lesional cutoff size for the occurrence of PVEs in a clinical setting. METHODS: Retrospective assessment of 51 PET/CT examinations (16-slice PET/CT device) for malignant PET foci was carried out. In all foci, the maximal standardized uptake value (SUVmax) and maximal lesion diameter on axial CT was documented. Determined SUVmax and lesional sizes were correlated via LOESS regression. In the resulting curve, the cutoff point for SUVmax size dependency was determined visually and mathematically using 2 approximating straight lines. RESULTS: In 45 patients, 313 of 413 PET foci found were malignant, measurable on CT and had a roughly spherical geometry (SUVmax: 2.5-103.3, mean ± SD 20.5 ± 15.18; CT diameter: 5-103 mm, mean ± SD 21.8 ± 13.1 mm). The cutoff lesional size for the occurrence of PVEs was 20.4 mm by the mathematical approach and 25 mm by visual assessment. CONCLUSION: In (68)Ga-DOTATOC imaging, the clinical lesional size threshold is far larger than expected from systemic resolution only. Thus, tracer uptake quantification is only acceptable in sufficiently large lesions.

Performance of Deauville Criteria in [18F]FDG-PET/CT Diagnostics of Giant Cell Arteritis.

In this retrospective study, PET/CT data from 59 patients with suspected giant cell arteritis (GCA) were reviewed using the Deauville criteria to determine an optimal cut-off between PET positivity and negativity. Seventeen standardised vascular regions were analysed per patient by three investigators blinded to clinical information. Statistical analysis included ROC curves with areas under the curve (AUC), Cohen's and Fleiss' kappa (κ) to calculate sensitivity, specificity, accuracy, and agreement. According to final clinician's diagnosis and the revised 2017 ACR criteria GCA was confirmed in 29 of 59 (49.2 %) patients. With a diagnostic cut-off ≥ 4 (highest tracer uptake of a vessel wall exceeds liver uptake) for PET positivity, all investigators achieved high accuracy (range, 89.8-93.2%) and AUC (range, 0.94-0.97). Sensitivity and specificity ranged from 89.7-96.6% and 83.3-96.7%, respectively. Agreement between the three investigators suggested 'almost perfect agreement' (Fleiss' κ = 0.84) A Deauville score of ≥4 as threshold for PET positivity yielded excellent results with high accuracy and almost perfect inter-rater agreement, suggesting a standardized, reproducible, and reliable score in diagnosing GCA. However, the small sample size and reference standard could lead to biases. Therefore, verification in a multicentre study with a larger patient cohort and prospective setting is needed.

ChatGPT: Can You Prepare My Patients for [18F]FDG PET/CT and Explain My Reports?

We evaluated whether the artificial intelligence chatbot ChatGPT can adequately answer patient questions related to [18F]FDG PET/CT in common clinical indications before and after scanning. Methods: Thirteen questions regarding [18F]FDG PET/CT were submitted to ChatGPT. ChatGPT was also asked to explain 6 PET/CT reports (lung cancer, Hodgkin lymphoma) and answer 6 follow-up questions (e.g., on tumor stage or recommended treatment). To be rated "useful" or "appropriate," a response had to be adequate by the standards of the nuclear medicine staff. Inconsistency was assessed by regenerating responses. Results: Responses were rated "appropriate" for 92% of 25 tasks and "useful" for 96%. Considerable inconsistencies were found between regenerated responses for 16% of tasks. Responses to 83% of sensitive questions (e.g., staging/treatment options) were rated "empathetic." Conclusion: ChatGPT might adequately substitute for advice given to patients by nuclear medicine staff in the investigated settings. Improving the consistency of ChatGPT would further increase reliability.