Metabolic tumor heterogeneity analysis by F-18 FDG PET/CT predicts mediastinal lymph node metastasis in non-small cell lung cancer patients with clinically suspected N2

Impact of preoperative [18F]FDG PET/CT vs. contrast-enhanced CT in the staging and survival of patients with clinical stage I and II non-small cell lung cancer: a 10-year follow-up study

OBJECTIVES

To elucidate the impact of [18F]FDG positron emission tomography/computed tomography (PET/CT) vs. CT workup on staging and prognostic evaluation of clinical stage (c) I-II NSCLC.

METHODS

We retrospectively identified 659 cI-II NSCLC who underwent CT (267 patients) or preoperative CT followed by PET/CT (392 patients), followed by curative-intended complete resection in our hospital from January 2008 to December 2013. Differences were assessed between preoperative and postoperative stage. Five-year disease-free survival (DFS) and overall survival (OS) rates were calculated using the Kaplan-Meier approach and compared with log-rank test. Impact of preoperative PET/CT on survival was assessed by Cox regression analysis.

RESULTS

The study included 659 patients [mean age, 59.5 years ± 10.8 (standard deviation); 379 men]. The PET/CT group was superior over CT group in DFS [12.6 vs. 6.9 years, HR 0.67 (95% CI 0.53-0.84), p < 0.001] and OS [13.9 vs. 10.5 years, HR 0.64 (95% CI 0.50-0.81), p < 0.001]. In CT group, more patients thought to have cN0 migrated to pN1/2 disease as compared with PET/CT group [26.4% (66/250) vs. 19.2% (67/349), p < 0.001], resulting in more stage cI cases being upstaged to pII-IV [24.7% (49/198) vs. 16.1% (47/292), p = 0.02], yet this was not found in cII NSCLC [27.5% (19/69) vs. 27.0% (27/100), p = 0.94]. Cox regression analysis identified preoperative PET/CT as an independent prognostic factor of OS and DFS (p = 0.002, HR = 0.69, 95% CI 0.54-0.88; p = 0.004, HR = 0.72, 95% CI 0.58-0.90).

CONCLUSION

Addition of preoperative [18F]FDG PET/CT was associated with superior DFS and OS in resectable cI-II NSCLC, which may result from accurate staging and stage-appropriate 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.

Predictive value of multiple metabolic and heterogeneity parameters of 18F-FDG PET/CT for EGFR mutations in non-small cell lung cancer

OBJECTIVES

To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and ¹⁸F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve.

RESULTS

The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter.

CONCLUSION

Among the metabolic and heterogeneity parameters of ¹⁸F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features.

The diagnostic value of metabolic, morphological and heterogeneous parameters of 18F-FDG PET/CT in mediastinal lymph node metastasis of non-small cell lung cancer

OBJECTIVES

To investigate the value of PET/CT metabolic, morphological and heterogeneous parameters in the diagnosis of 18F-FDG positive mediastinal lymph node metastasis in non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

A total of 156 patients with pathologically diagnosed NSCLC and underwent 18F-FDG PET/CT scans were enrolled in this study. Mediastinal lymph nodes with 18F-FDG uptake greater than the mediastinum were analyzed. The metabolic parameters of maximum and mean standardized uptake value (SUVmax, SUVmean), SUVratio (node SUVmax/mediastinum SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), morphological parameters of maximum short diameter (Dmin), CT values and metabolic heterogeneity parameter of coefficient of variation (COV) were measured. The performance of each parameter and their combinations for diagnosis of lymph node metastasis was evaluated through receiver operating characteristic (ROC) curves and binary logistic regression analysis.

RESULTS

There were 206 lymph nodes with pathological evidence included in the study, including 103 metastatic and 103 nonmetastatic nodes. The SUVmax, SUVmean, SUVratio, TLG, COV and Dmin of metastatic lymph nodes were significantly higher/greater than those in nonmetastatic ones (P < 0.05). ROC curve analysis revealed that the combination of SUVratio, Dmin and COV showed the highest diagnostic efficacy among all single and combined parameters, the area under the curve (AUC) was 0.907 (P = 0.000), these three parameters all increased the risk of lymph node metastasis, with odds ratios of 1.848, 1.293 and 1.258, respectively (all P < 0.05).

CONCLUSION

Heterogeneity parameter was helpful for the accurate distinction of mediastinal lymph node metastasis in NSCLC. The combination of the SUVratio, Dmin and COV could improve the diagnostic accuracy. Multiple-parameters analysis plays an important complementary role in the diagnosis of lymph node metastasis.

High metabolic heterogeneity on baseline 18FDG-PET/CT scan as a poor prognostic factor for newly diagnosed diffuse large B-cell lymphoma

Metabolic heterogeneity (MH) can be measured using 18F-fluorodeoxyglucose (18FDG) positron emission tomography/computed tomography (PET/CT), and it indicates an inhomogeneous tumor microenvironment. High MH has been shown to predict a worse prognosis for primary mediastinal B-cell lymphoma, whereas its prognostic value in diffuse large B-cell lymphoma (DLBCL) remains to be determined. In the current study, we investigated the prognostic values of MH evaluated in newly diagnosed DLBCL. In the training cohort, 86 patients treated with cyclophosphamide, doxorubicin, vincristine, and prednisone-like chemotherapies were divided into low-MH and high-MH groups using receiver operating characteristic analysis. MH was not correlated with metabolic tumor volume of the corresponding lesion, indicating that MH was independent of tumor burden. At 5 years, overall survivals were 89.5% vs 61.2% (P = .0122) and event-free survivals were 73.1% vs 51.1% (P = .0327) in the low- and high-MH groups, respectively. A multivariate Cox-regression analysis showed that MH was an independent predictive factor for overall survival. The adverse prognostic impacts of high MH were confirmed in an independent validation cohort with 64 patients. In conclusion, MH on baseline 18FDG-PET/CT scan predicts treatment outcomes for patients with newly diagnosed DLBCL.

18F-FDG PET/CT Scans Can Identify Sub-Groups of NSCLC Patients with High Glucose Uptake in the Majority of Their Tumor Lesions

Background: Reprogrammed glucose metabolism is a hallmark of cancer making it an attractive therapeutic target, especially in cancers with high glucose uptake such as non-small cell lung cancer (NSCLC). Tools to select patients with high glucose uptake in the majority of tumor lesions are essential in the development of anti-cancer drugs targeting glucose metabolism. Type 2 diabetes mellitus (T2DM) patients may have tumors highly dependent on glucose uptake. Surprisingly, this has not been systematically studied. Therefore, we aimed to determine which patient and tumor characteristics, including concurrent T2DM, are related to high glucose uptake in the majority of tumor lesions in NSCLC patients as measured by 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) positron emission tomography (PET)/computed tomography (CT) scans.

Methods: Routine primary diagnostic ¹⁸F-FDG PET/CT scans of consecutive NSCLC patients were included. Mean standardized uptake value (SUVmean) of ¹⁸F-FDG was determined for all evaluable tumor lesions and corrected for serum glucose levels according to the European Association of Nuclear Medicine Research Ltd guidelines. Patient characteristics potentially determining degree of tumor lesion glucose uptake in the majority of tumor lesions per patient were investigated.

Results: The cohort consisted of 102 patients, 28 with T2DM and 74 without T2DM. The median SUVmean per patient ranged from 0.8 to 35.2 (median 4.2). T2DM patients had higher median glucose uptake in individual tumor lesions and per patient compared to non-diabetic NSCLC patients (SUVmean 4.3 vs 2.8, P < 0.001 and SUVmean 5.4 vs 3.7, P = 0.009, respectively). However, in multivariable analysis, high tumor lesion glucose uptake was only independently determined by number of tumor lesions ≥1 mL per patient (odds ratio 0.8, 95% confidence interval 0.7-0.9).

Conclusions:¹⁸F-FDG PET/CT scans can identify sub-groups of NSCLC patients with high glucose uptake in the majority of their tumor lesions. T2DM patients had higher tumor lesion glucose uptake than non-diabetic patients. However, this was not independent of other factors such as the histological subtype and number of tumor lesions per patient.

Machine learning-based diagnostic method of pre-therapeutic 18F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer

OBJECTIVES

We aimed to find the best machine learning (ML) model using ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for evaluating metastatic mediastinal lymph nodes (MedLNs) in non-small cell lung cancer, and compare the diagnostic results with those of nuclear medicine physicians.

METHODS

A total of 1329 MedLNs were reviewed. Boosted decision tree, logistic regression, support vector machine, neural network, and decision forest models were compared. The diagnostic performance of the best ML model was compared with that of physicians. The ML method was divided into ML with quantitative variables only (MLq) and adding clinical information (MLc). We performed an analysis based on the ¹⁸F-FDG-avidity of the MedLNs.

RESULTS

The boosted decision tree model obtained higher sensitivity and negative predictive values but lower specificity and positive predictive values than the physicians. There was no significant difference between the accuracy of the physicians and MLq (79.8% vs. 76.8%, p = 0.067). The accuracy of MLc was significantly higher than that of the physicians (81.0% vs. 76.8%, p = 0.009). In MedLNs with low ¹⁸F-FDG-avidity, ML had significantly higher accuracy than the physicians (70.0% vs. 63.3%, p = 0.018).

CONCLUSION

Although there was no significant difference in accuracy between the MLq and physicians, the diagnostic performance of MLc was better than that of MLq or of the physicians. The ML method appeared to be useful for evaluating low metabolic MedLNs. Therefore, adding clinical information to the quantitative variables from ¹⁸F-FDG PET/CT can improve the diagnostic results of ML.

KEY POINTS

• Machine learning using two-class boosted decision tree model revealed the highest value of area under curve, and it showed higher sensitivity and negative predictive values but lower specificity and positive predictive values than nuclear medicine physicians. • The diagnostic results from machine learning method after adding clinical information to the quantitative variables improved accuracy significantly than nuclear medicine physicians. • Machine learning could improve the diagnostic significance of metastatic mediastinal lymph nodes, especially in mediastinal lymph nodes with low 18F-FDG-avidity.

Prediction of mediastinal lymph node metastasis based on 18F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer

OBJECTIVE

The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC).

METHOD

Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application.

RESULTS

The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected.

CONCLUSION

An SVM model based on ¹⁸F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.

KEY POINTS

• The SVM model based on ¹⁸F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC. • The SUR_blood plays a major role in the SVM model. • The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.

Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

BACKGROUND

Cholangiocarcinoma (CCA) is a relatively rare malignant biliary system tumor, and yet it represents the second most common primary hepatic neoplasm, following hepatocellular carcinoma. Regardless of the type, location, or etiology, the survival prognosis of these tumors remains poor. The only method of cure for CCA is complete surgical resection, but part of patients with complete resection are still subject to local recurrence or distant metastasis.

SUMMARY

Over the last several decades, our understanding of the molecular biology of CCA has increased tremendously, diagnostic and evaluative techniques have evolved, and novel therapeutic approaches have been established. Key Messages: This review provides an overview of preoperative imaging evaluations of CCA. Furthermore, relevant information about artificial intelligence (AI) in medical imaging is discussed, as well as the development of AI in CCA treatment.

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

OBJECTIVES

The aim of our study was to investigate the value of a simple metabolic heterogeneity parameter, COV (coefficient of variation), by 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in the prognosis prediction of central lung cancer in early and locally advanced non-small-cell lung cancer (NSCLC).

METHODS

Seventy-three patients with NSCLC manifesting as central lung cancer were included retrospectively, and we used the COV to evaluate metabolic heterogeneity. Univariate and multivariate analyses were used to evaluate the predictive value in terms of overall survival (OS) and progression-free survival (PFS).

RESULT

For all 73 patients with pathologically confirmed NSCLC, 69.9 % had SCC, and 30.1 % had ADC or other types of NSCLC. The COV was a statistically significant factor in the univariate analysis for the OS rate. The optimal cut-off value was 23.1366, with sensitivity = 0.737 and specificity = 0.771. The COV values were dichotomized by this value and included with atelectasis in the Cox multivariate analysis. Both COV and atelectasis were independent risk factors for OS as follows: for COV (HR, 3.162, P = 0.0002), the 2-year OS rate was 62.5 % and 26.9 % in the low and high COV groups, respectively. For atelectasis (HR 2.047, P = 0.041), the 2-year OS rate was 30.6 % and 65.2 % in the groups with and without atelectasis, respectively (P = 0.017). For PFS, only COV (HR, 2.636, P = 0.001) was a significant predictor. The 2-year PFS rate was 29.7 % in the low COV group and 8% in the high COV group.

CONCLUSION

The pre-treatment metabolic heterogeneity parameter COV is a simple and easy way to predict the OS and PFS of patients with NSCLC manifesting as central lung cancer. Therefore, COV plays an important role in prognostic risk classification in NSCLC. The presence of atelectasis could also be a risk factor for poor prognosis of OS.

Preoperative staging of the mediastinum is an essential and multidisciplinary task

Mediastinal staging is a crucial step in the management of patients with NSCLC. With the recent development of novel techniques, mediastinal staging has evolved from an activity of interest mainly for thoracic surgeons to a joint effort carried out by many specialists. In this regard, the debate of cases in MDT sessions is crucial for optimal management of patients. Current evidence-based clinical guidelines for preoperative NSCLC staging recommend that mediastinal staging should be performed with increasing invasiveness. Image-based techniques are the first approach, although they have limited accuracy and findings must be confirmed by pathology in almost all cases. In this setting, the advent of radiomics is promising. Invasive staging depends on procedural factors rather than diagnostic performance. The choice between endoscopy-based or surgical procedures should depend on the local expertise of each centre. As the extension of mediastinal disease in terms of number of involved lymph nodes and nodal stations affects prognosis and the choice of treatment, systematic samplings are preferred over random targeted samplings. Following this approach, a diagnosis of single mediastinal nodal involvement can be unreliable if all reachable mediastinal nodal stations have not been assessed. The performance of confirmatory mediastinoscopy after a negative endoscopy-based procedure is controversial but currently recommended. Current indications of invasive staging in patients with radiologically normal mediastinum have to be re-evaluated, especially for central tumour location.

Drugging cancer metabolism: Expectations vs. reality

As compared to their normal counterparts, neoplastic cells exhibit a variety of metabolic changes that reflect not only genetic and epigenetic defects underlying malignant transformation, but also the nutritional and immunobiological conditions of the tumor microenvironment. Such alterations, including the so-called Warburg effect (an increase in glucose uptake largely feeding anabolic and antioxidant metabolism), have attracted considerable attention as potential targets for the development of novel anticancer therapeutics. However, very few drugs specifically conceived to target bioenergetic cancer metabolism are currently approved by regulatory agencies for use in humans. This reflects the elevated degree of heterogeneity and redundancy in the metabolic circuitries exploited by neoplastic cells from different tumors (even of the same type), as well as the resemblance of such metabolic pathways to those employed by highly proliferating normal cells. Here, we summarize the major metabolic alterations that accompany oncogenesis, the potential of targeting bioenergetic metabolism for cancer therapy, and the obstacles that still prevent the clinical translation of such a promising therapeutic paradigm.

Comment on "Metabolic tumor heterogeneity analysis by F-18 FDG PET/CT predicts mediastinal lymph node metastasis in non-small cell lung cancer patients with clinically suspected N2"

With great interest, we read the article "metabolic tumor heterogeneity analysis by F-18 FDG PET/CT predicts mediastinal lymph node metastasis in non-small cell lung cancer patients with clinically suspected N2"(by Kisoo Pahk et al., 2018). And we would like to thank the authors for this highly useful work, which raises a few points worthy of discussion.