PET-Based Volumetric Biomarkers for Risk Stratification of Non-Small Cell Lung Cancer Patients

Association Between Pretreatment 11C-Methionine Positron Emission Tomography Metrics, Histology, and Prognosis in 125 Newly Diagnosed Patients with Adult-Type Diffuse Glioma Based on the World Health Organization 2021Classification

OBJECTIVE

To clarify the relationships between 11C-methionine (MET) positron emission tomography (PET) metrics and the histology, genetics, and prognosis of adult-type diffuse glioma (ADG) based on the World Health Organization (WHO) 2021 classification.

METHODS

A total of 125 newly diagnosed patients with ADG were enrolled. We compared the maximum standardized uptake value (SUVmax), tumor-to-normal background ratio (TNR), metabolic tumor volume (MTV), and total lesion methionine uptake (TLMU) to the histology and genetics of the patients with ADG. We also evaluated the prognoses of the 93 surgically treated patients.

RESULTS

The patients with isocitrate dehydrogenase wild ADG showed significantly higher MET-PET metrics (P < 0.05 for all parameters), significantly shorter overall survival and progression-free survival (P < 0.0001 for both) than those of the patients with isocitrate dehydrogenase mutant (IDHm) ADG. In the IDHm ADG group, the SUVmax, MTV, and TLMU values were significantly higher in patients with IDHm grade (G) 4 astrocytoma than patients with IDHm G2/3 astrocytoma (P < 0.05 for all), but not than patients with G2-3 oligodendroglioma. The progression-free survival was significantly shorter in the patients with G4 astrocytoma versus the patients with G2/3 astrocytoma and G3 oligodendroglioma (P < 0.05 for both). The SUVmax and TNR values were significantly higher in recurrent patients than nonrecurrent patients (P < 0.01 for both), but no significant differences were found in MTV or TLMU values.

CONCLUSIONS

MET-PET metrics well reflect the histological subtype, WHO grade and prognosis of ADG based on the 2021 WHO classification, with the exception of oligodendroglial tumors. Volumetric parameters were not significantly associated with recurrence, unlike the SUVmax and TNR.

Deep Learning Features and Metabolic Tumor Volume Based on PET/CT to Construct Risk Stratification in Non-small Cell Lung Cancer

RATIONALE AND OBJECTIVES

To build a risk stratification by incorporating PET/CT-based deep learning features and whole-body metabolic tumor volume (MTVwb), which was to make predictions about overall survival (OS) and progression-free survival (PFS) for those with non-small cell lung cancer (NSCLC) as a complement to the TNM staging.

MATERIALS AND METHODS

The study enrolled 590 patients with NSCLC (413 for training and 177 for testing). Features were extracted by employing a convolutional neural network. The combined risk stratification (CRS) was constructed by the selected features and MTVwb, which were contrasted and integrated with TNM staging. In the testing set, those were verified.

RESULTS

Multivariate analysis revealed that CRS was an independent predictor of OS and PFS. C-indexes of the CRS demonstrated statistically significant increases in comparison to TNM staging, excepting predicting OS in the testing set (for OS, C-index=0.71 vs. 0.691 in the training set and 0.73 vs. 0.736 in the testing set; for PFS, C-index=0.702 vs. 0.686 in the training set and 0.732 vs. 0.71 in the testing set). The nomogram that combined CRS with TNM staging demonstrated the most superior model performance in the training and testing sets (C-index=0.741 and 0.771).

CONCLUSION

The addition of CRS improves TNM staging's predictive power and shows potential as a useful tool to support physicians in making treatment decisions.

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

OBJECTIVE

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.

METHODS

We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.

RESULTS

In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).

CONCLUSIONS

Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Evaluation of therapeutic effect and prognostic value of 18F-FDG PET/CT in different treatment nodes of DLBCL patients

BACKGROUND

In the present study, we aimed to investigate the role of baseline (B), interim (I) and end-of-treatment (Eot) 18F-FDG PET/CT in assessing the prognosis of diffuse large B cell lymphoma (DLBCL), so as to identify patients who need intensive treatment at an early stage.

METHODS

A total of 127 DLBCL patients (62 men; 65 women; median age 62 years) were retrospectively analyzed in this study. Baseline (n = 127), interim (n = 127, after 3-4 cycles) and end-of-treatment (n = 53, after 6-8 cycles) PET/CT images were re-evaluated; semi-quantitative parameters such as maximum standardized uptake value of lesion-to-liver ratio (SUVmax(LLR)) and lesion-to-mediastinum ratio (SUVmax(LMR)), total metabolic tumor volume (TMTV) and total metabolic tumor volume (TLG) were recorded. ΔTLG1 was the change of interim relative to baseline TLG (I to B), ΔTLG2 (Eot to B). ΔSUVmax and ΔTMTV were the same algorithm. The visual Deauville 5-point scale (D-5PS) has been adopted as the major criterion for PET evaluation. Visual analysis (VA) and semi-quantitative parameters were assessed for the ability to predict progression-free survival (PFS) and overall survival (OS) by using Kaplan-Meier method, cox regression and logistic regression analysis. When visual and semi-quantitative analysis are combined, the result is only positive if both are positive.

RESULTS

At a median follow-up of 34 months, the median PFS and OS were 20 and 32 months. The survival curve analysis showed that advanced stage and IPI score with poor prognosis, ΔSUVmax(LLR)1 < 89.2%, ΔTMTV1 < 91.8% and ΔTLG1 < 98.8%, ΔSUVmax(LLR)2 < 86.4% were significantly related to the shortening of PFS in patient (p < 0.05). ΔSUVmax(LLR)1 < 83.2% and ΔTLG1 < 97.6% were significantly correlated with the shortening of OS in patients (p < 0.05). Visual analysis showed that incomplete metabolic remission at I-PET and Eot-PET increased the risk of progress and death. In terms of predicting recurrence by I-PET, the combination of visual and semi-quantitative parameters showed higher positive predictive value (PPV) and specificity than a single index.

CONCLUSION

Three to four cycles of R-CHOP treatment may be a time point for early prediction of early recurrence/refractory (R/R) patients and active preemptive treatment. Combined visual analysis with semi-quantitative parameters of 18F-FDG PET/CT at interim can improve prognostic accuracy and may allow for more precise screening of patients requiring early intensive therapy.

Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors

Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of 18F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone 18F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTVTOT) and Whole-Body TLG (TLGWB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan-Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTVTOT (p = 0.0007), TLGWB (p = 0.0129) and stage (p = 0.0122). Using Kaplan-Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients.

Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer

We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from ¹⁸F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent ¹⁸F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTV_TOT) and whole-body Total Lesion Glycolysis (TLG_WB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTV_TOT, TLG_WB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTV_TOT (p = 0.0050), TLG_WB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTV_TOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTV_TOT ≤ 89.5 mL had higher OS than those with MTV_TOT > 89.5 mL (p = 0.0063). Combining CoV and MTV_TOT, patients with CoV ≤ 0.38 and MTV_TOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.

Metabolic phenotypes, serum tumor markers, and histopathological subtypes in predicting bone metastasis: analysis of 695 patients with lung cancer in China

Background

Patients with lung cancer who develop bone metastasis (BM) generally have an adverse prognosis. Although several clinical models have been used to predict BM in patients with lung cancer, the results are unsatisfactory. In this retrospective study, we investigated the role of ¹⁸F-2-fluoro-2-deoxyglucose (FDG) metabolic activity, serum tumor markers, and histopathological subtypes in predicting BM in patients with lung cancer.

Methods

This study included 695 consecutive patients with lung cancer who underwent ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) and in whom serum tumor markers were detected prior to treatment. The maximum standardized uptake value of primary tumors (pSUV_max), metastatic lymph nodes (nSUV_max) and distant metastases (mSUV_max), 8 serum tumor markers [carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCCA), cytokeratin 19 fragment (CYFRA21-1), carbohydrate antigen (CA) 125, CA50, CA72-4, and ferritin], and histopathological subtypes were compared between patients with and without BM. Receiver operating characteristic (ROC) curve and multiple logistic regression analyses were performed to identify predictors of BM in patients with lung cancer.

Results

BM was identified in 133 (19.1%) patients and not in 562 (80.9%). Patients with BM had significantly higher pSUV_max, nSUV_max, and mSUV_max than did those without BM. High concentrations of 6 serum tumor markers (i.e., CEA, ferritin, NSE, CA50, CA125, and CYFRA21-1) were significantly associated with BM. There were significant differences in the proportion of histopathological subtypes between patients with and without BM (χ²=32.35; P<0.001). The area under ROC-derived curve based on metabolic parameters was 0.737 (95% CI: 0.644-0.829) and 0.884 (95% CI: 0.825-0.943) when combined with the 6 serum tumor markers and histopathological subtypes, respectively.

Conclusions

High pSUV_max, nSUV_max, and mSUV_max favor the presence of BM in patients with lung cancer, and serum tumor markers and histopathological subtypes are important factors for predicting BM in these patients.

Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma

OBJECTIVE

We investigated whether glycolytic heterogeneity correlated with histopathology, and further stratified the survival outcomes pertaining to resectable lung adenocarcinoma.

METHODS

We retrospectively analyzed the ¹⁸F-fluorodeoxyglucose positron emission tomography-derived entropy and histopathology from 128 patients who had undergone curative surgery for lung adenocarcinoma. Disease-free survival (DFS) and overall survival (OS) were analyzed using univariate and multivariate Cox regression models. Independent predictors were used to construct survival prediction models.

RESULTS

Entropy significantly correlated with histopathology, including tumor grades, lympho-vascular invasion, and visceral pleural invasion. Furthermore, entropy was an independent predictor of unfavorable DFS (p = 0.031) and OS (p = 0.004), while pathological nodal metastasis independently predicted DFS (p = 0.009). Our entropy-based models outperformed the traditional staging system (c-index = 0.694 versus 0.636, p = 0.010 for DFS; c-index = 0.704 versus 0.630, p = 0.233 for OS). The models provided further survival stratification in subgroups comprising different tumor grades (DFS: HR = 2.065, 1.315, and 1.408 for grade 1-3, p = 0.004, 0.001, and 0.039, respectively; OS: HR = 25.557, 6.484, and 2.570, for grade 1-3, p = 0.006, < 0.001, and = 0.224, respectively).

CONCLUSION

The glycolytic heterogeneity portrayed by entropy is associated with aggressive histopathological characteristics. The proposed entropy-based models may provide more sophisticated survival stratification in addition to histopathology and may enable personalized treatment strategies for resectable lung cancer.

The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease

OBJECTIVE

To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease.

METHODS

We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival.

RESULTS

Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies.

CONCLUSION

Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.

Can positron emission tomography–computed tomography-based three target lesions' total lesion glycolysis predict therapeutic response in Hodgkin Lymphoma?

Background

Universally maximum standardized uptake value (SUVmax) and lactate dehydrogenase (LDH) are used as tools for response assessment in Hodgkin Lymphoma (HL) patients. Our objectives are to evaluate the predictive potential and response assessment of total lesion glycolysis (TLG) and metabolic tumor volume (MTV)—maximum three target lesions—as another alternatives and to investigate the correlation between TLG and MTV with LDH.

Results

Both initial SUVmax and TLG were significantly associated with early patient response (p value 0.03, 0.047, respectively). An optimal threshold for SUVmax and TLG less than or equal 19.52, and 158.6, respectively, correlated with better therapeutic response. Initial LDH was moderately correlated with initial values of TLG (rs = 0.4, p value 0.01), MTV (rs = 0.44, p value 0.01) and SUVmax (rs = 0.42, p value 0.01).

Conclusion

TLG in correlation with LDH can be significant prognostic factors of therapeutic response in HL. They can be used for the identification of a subset of HL patients with a better outcome.

Predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT for brain metastases in patients with locally advanced non-small-cell lung cancer

OBJECTIVES

Brain metastases (BMs) are a major cause leading to the failure of treatment management for non-small-cell lung cancer (NSCLC) patients. The purpose of this study was to evaluate the predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT measured with metabolic tumor volume (MTV) and total lesion glycolysis (TLG) for brain metastases (BMs) development in patients with locally advanced non-small-cell lung cancer (NSCLC) after treatment.

METHODS

Forty-seven patients with stage IIB-IIIC NSCLC who underwent baseline 18F-FDG PET/CT examinations were retrospectively reviewed. The maximum standardized uptake value (SUVmax), MTV, and TLG of the primary tumor (SUVmaxT, MTVT, and TLGT), metastatic lymph nodes (SUVmaxN, MTVN, and TLGN), and whole-body tumors (SUVmaxWB, MTVWB, and TLGWB) were measured. The optimal cut-off values of PET parameters to predict brain metastasis-free survival were obtained using Receiver operating characteristic (ROC) analysis, and the predictive value of clinical variables and PET parameters were evaluated using Cox proportional hazards regression analysis.

RESULTS

The median follow-up duration was 25.0 months for surviving patients, and 13 patients (27.7%) developed BM. The optimal cut-off values were 21.1 mL and 150.0 g for MTVT and TLGT, 20.0, 10.9 mL and 55.6 g for SUVmaxN, MTVN and TLGN, and 27.9, 27.4 mL and 161.0 g for SUVmaxWB, MTVWB and TLGWB, respectively. In the Cox proportional hazards models, the risk of BM was significantly associated with MTVN and MTVWB or TLGN and TLGWB after adjusting for histological cell type, N stage, SUVmaxN, and SUVmaxWB.

CONCLUSIONS

Baseline metabolic tumor burden (MTV and TLG) evaluated from the level of metastatic lymph nodes and whole-body tumors are significant predictive factors for BM development in patients with locally advanced NSCLC.

Tumor metabolic and secondary lymphoid organ metabolic markers on 18F-fludeoxyglucose positron emission tomography predict prognosis of immune checkpoint inhibitors in advanced lung cancer

Background

The purpose of this study was to investigate the predictive value of tumor metabolic parameters in combination with secondary lymphoid metabolic parameters on positron emission tomography (PET)/computed tomography (CT) for immune checkpoint inhibitor (ICI) prognosis in advanced lung cancer.

Methods

This study retrospectively included 125 patients who underwent 18F-fludeoxyglucose (FDG) PET/CT before ICI therapy, including 41 patients who underwent a second PET/CT scan during ICI treatment. The measured PET/CT parameters included tumor metabolism parameters [maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), total lesion glycolysis (TLG), and total metabolic tumor volume (TMTV)] and secondary lymphoid organ metabolism parameters [spleen-to-liver SUVmax ratio (SLR) and bone marrow-to-liver SUVmax ratio (BLR)]. The correlation of PET/CT metabolic parameters with early ICI treatment response, progression-free survival (PFS), and overall survival (OS) was analyzed.

Results

Within a median follow-up of 28.7 months, there were 44 responders and 81 non-responders. The median PFS was 8.6 months (95% confidence interval (CI): 5.872–11.328), and the median OS was 20.4 months (95% CI: 15.526–25.274). Pretreatment tumor metabolic parameters were not associated with early treatment responses. The high bone marrow metabolism (BLR &gt;1.03) was significantly associated with a shorter PFS (p = 0.008). Patients with a high TMTV (&gt;168 mL) and high spleen metabolism (SLR &gt;1.08) had poor OS (p = 0.019 and p = 0.018, respectively). Among the 41 patients who underwent a second PET/CT scan, the ΔSUVmax was significantly lower (p = 0.01) and the SLR was significantly higher (p = 0.0086) in the responders. Populations with low-risk characteristics (low TMTV, low SLR, and ΔSLR &gt; 0) had the longest survival times.

Conclusion

High pretreatment TMTV and SLR are associated with poor OS, and increased spleen metabolism after ICI therapy predicts treatment benefit. This indicates that the combination of tumor and spleen metabolic parameters is a valuable prognostic strategy.

Volumetric parameters from [18 F]FDG PET/CT predicts survival in patients with high-grade gastroenteropancreatic neuroendocrine neoplasms

A positive fluorine-18 labelled 2-deoxy-2-fluoroglucose ([¹⁸ F]FDG) positron emission tomography/computed tomography (PET/CT) has been associated with more aggressive disease and less differentiated neuroendocrine neoplasms (NEN). Although a high maximum standardized uptake value (SUV_max ) predicts poor outcome in NEN, volumetric parameters from [¹⁸ F]FDG PET have not been evaluated for prognostication in a pure high-grade gastroenteropancreatic (GEP) NEN cohort. In this retrospective observational study, we evaluated the volumetric PET parameters total metabolic tumour volume (tMTV) and total total lesion glycolysis (tTLG) for independent prognostication of overall survival (OS). High-grade GEP NEN patients with [¹⁸ F]FDG PET/CT examination and biopsy within 90 days were included. Total MTV and tTLG were calculated using an adaptive thresholding software. Patients were dichotomised into low and high metabolic groups based on median tMTV and tTLG. OS was compared using Kaplan-Meier estimator and log-rank test. Uni and multivariable Cox regression was used to estimate effect sizes and adjust for tumour differentiation and SUV_max . Sixty-six patients (median age 64 years) were included with 14 NET G3 and 52 NEC cases after histological re-evaluation. Median tMTV was 208 cm³ and median tTLG 1899 g. Median OS in the low versus high tMTV-group was 21.2 versus 5.7 months (HR 2.53, p = 0.0007) and 22.8 versus 5.7 months (HR 2.42, p = 0.0012) in the tTLG-group. Adjusted for tumour differentiation and SUV_max , tMTV and tTLG still predicted for poor OS, and both tMTV and tTLG were stronger prognostic parameters than SUV_max . Both regression models showed a strong association between volumetric parameters and OS for both neuroendocrine tumours (NET) G3 and neuroendocrine carcinomas (NEC). OS for the tTLG low metabolic NEC was much higher than for the tTLG high metabolic NET G3 (18.3 vs. 5.7 months). High-grade GEP NEN patients with high tMTV or tTLG had a worse OS regardless of tumour differentiation (NET G3 or NEC). Volumetric PET parameters were stronger prognostic parameters than SUV_max .

Prognostic Value of Combing Primary Tumor and Nodal Glycolytic-Volumetric Parameters of 18F-FDG PET in Patients with Non-Small Cell Lung Cancer and Regional Lymph Node Metastasis

We investigated whether the combination of primary tumor and nodal ¹⁸F-FDG PET parameters predict survival outcomes in patients with nodal metastatic non-small cell lung cancer (NSCLC) without distant metastasis. We retrospectively extracted pre-treatment ¹⁸F-FDG PET parameters from 89 nodal-positive NSCLC patients (stage IIB–IIIC). The Cox proportional hazard model was used to identify independent prognosticators of overall survival (OS) and progression-free survival (PFS). We devised survival stratification models based on the independent prognosticators and compared the model to the American Joint Committee on Cancer (AJCC) staging system using Harrell’s concordance index (c-index). Our results demonstrated that total TLG (the combination of primary tumor and nodal total lesion glycolysis) and age were independent risk factors for unfavorable OS (p < 0.001 and p = 0.001) and PFS (both p < 0.001), while the Eastern Cooperative Oncology Group scale independently predicted poor OS (p = 0.022). Our models based on the independent prognosticators outperformed the AJCC staging system (c-index = 0.732 versus 0.544 for OS and c-index = 0.672 versus 0.521 for PFS, both p < 0.001). Our results indicate that incorporating total TLG with clinical factors may refine risk stratification in nodal metastatic NSCLC patients and may facilitate tailored therapeutic strategies in this patient group.