The diagnostic ability of 18F-FDG PET/CT for mediastinal lymph node staging using 18F-FDG uptake and volumetric CT histogram analysis in non-small cell lung cancer

Epithelial Malignant Pleural Mesothelioma Mimics Lymphoma on 18 F-FDG PET/MRI : A Case Report

ABSTRACT

Malignant pleural mesothelioma is a rare tumor with a poor prognosis. We describe a case of 55-year-old man without asbestos exposure history presenting with extensive lymph nodes with high 18 F-FDG uptake in PET/MRI but atypical pleural manifestations thereby being misdiagnosed for lymphoma. Pathological examination concludes for an epithelioid mesothelioma-associated lymph node metastasis. This case emphasizes that with the extensive lymph node abnormalities shown in PET imaging, in addition to the general consideration of lymphoma, it is still necessary to be vigilant about the possibility of mesothelioma and emphasizes the necessity of pathological examination.

A Multi-Modal Heterogeneous Graph Forest to Predict Lymph Node Metastasis of Non-Small Cell Lung Cancer

Lymph node metastasis (LNM) is critical for treatment decision-making for cancer patients, but it is difficult to diagnose accurately before surgery. Machine learning can learn nontrivial knowledge from multi-modal data to support accurate diagnosis. In this paper, we proposed a Multi-modal Heterogeneous Graph Forest (MHGF) approach to extract the deep representations of LNM from multi-modal data. Specifically, we first extracted the deep image features from CT images to represent the pathological anatomic extent of the primary tumor (pathological T stage) using a ResNet-Trans network. And then, a heterogeneous graph with six vertices and seven bi-directional relations was defined by medical experts to describe the possible relations between the clinical and image features. After that, we proposed a graph forest approach to construct the sub-graphs by removing each vertex in the complete graph iteratively. Finally, we used graph neural networks to learn the representations of each sub-graph in the forest to predict LNM and averaged all the prediction results as final results. We conducted experiments on 681 patients' multi-modal data. The proposed MHGF achieves the best performances with a 0.806 AUC value and 0.513 AP value compared with state-of-art machine learning and deep learning methods. The results indicate that the graph method can explore the relations between different types of features to learn effective deep representations for LNM prediction. Moreover, we found that the deep image features about the pathological anatomic extent of the primary tumor are useful for LNM prediction. And the graph forest approach can further improve the generalization ability and stability of the LNM prediction model.

Transesophageal Endoscopic Ultrasound Fine Needle Biopsy for the Diagnosis of Mediastinal Masses: A Retrospective Real-World Analysis

Background: Endoscopic ultrasound (EUS) plays an important role in the diagnosis and staging of thoracic disease. Our report studies the diagnostic performance and clinical impact of EUS fine needle aspiration (FNA) in a homogenous cohort of patients according to the distribution of the enlarged MLNs or pulmonary masses. Methods: We retrospectively reviewed the diagnostic performance of 211 EUS-FNA in 200 consecutive patients with enlarged or PET-positive MLNs and para-mediastinal masses who were referred to our oncological center between January 2019 and May 2020. Results: The overall sensitivity of EUS-FNA was 85% with a corresponding negative predictive value (NPV) of 56% and an accuracy of 87.5%. The sensitivity and accuracy in patients with abnormal MLNs were 81.1% and 84.4%, respectively. In those with para-mediastinal masses, sensitivity and accuracy were 96.4% and 96.8%. The accuracy for both masses and lymph nodes was 100%, and in the LAG (left adrenal gland), it was 66.6%. Conclusions: Our results show that, in patients with suspected mediastinal masses, EUS-FNA is an accurate technique to evaluate all reachable mediastinal nodal stations, including station 5.

Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter

Background

To investigate the value of ¹⁸F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm).

Methods

A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA).

Results

According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93–0.97) in the training group and 0.94 (95% CI: 0.89–0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM.

Conclusions

The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00895-x.

Prognostic Value of Dual-Time-Point [18F]FDG PET/CT for Predicting Distant Metastasis after Treatment in Patients with Non-Small Cell Lung Cancer

This study aimed to evaluate the prognostic significance of 2-Deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) uptake in the bone marrow (BM) and primary tumors on dual-time-point (DTP) PET/CT for predicting progression-free survival (PFS) and distant metastasis-free survival (DMFS) in patients with non-small cell lung cancer (NSCLC). We retrospectively analyzed DTP [18F]FDG PET/CT images from 211 patients with NSCLC. The maximum standardized uptake value (SUV) of primary lung cancer and mean [18F]FDG uptake of the BM (BM SUV) were measured from early and delayed PET/CT images, and the percent changes in these parameters (∆maximum SUV and ∆BM SUV) were calculated. On multivariate survival analysis, the maximum SUV and BM SUV on both early and delayed PET/CT scans were significantly associated with PFS, while the ∆maximum SUV and ∆BM SUV failed to show statistical significance. For DMFS, the ∆maximum SUV and ∆BM SUV were independent predictors along with the TNM stage. Distant progression was observed only in 1.3% of patients with low ∆maximum SUV and ∆BM SUV, whereas 28.2% of patients with high ∆maximum SUV and ∆BM SUV experienced distant progression. The ∆maximum SUV and ∆BM SUV on DTP [18F]FDG PET/CT were significant independent predictors for DMFS in patients with NSCLC.

Dual-Layer Spectral Detector CT in Comparison with FDG-PET/CT for the Assessment of Lymphoma Activity

PURPOSE

 In patients with malignant lymphoma, disease activity is recommended to be assessed by FDG-PET/CT and the Deauville five-point scale (5-PS). The purpose of this study was to explore the potential of iodine concentration measured in contrast-enhanced dual-layer spectral detector CT (SDCT) as an alternative surrogate parameter for lymphoma disease activity by investigating its correlation with maximum standardized uptake values (SUV_max) and 5-PS.

MATERIALS AND METHODS

 25 patients were retrospectively analyzed. Contrast-enhanced SDCT and FDG-PET/CT were performed in the same treatment interval within at most 3 months. CT attenuation values (AV), absolute iodine concentrations (aIC), and normalized iodine concentrations (nIC) of lymphoma lesions were correlated with SUV_max using Spearman's rank correlation coefficient. The performance of aIC and nIC to detect lymphoma activity (defined as 5-PS > 3) was determined using ROC curves.

RESULTS

 60 lesions were analyzed, and 31 lesions were considered active. AV, aIC, and nIC all correlated significantly with SUV_max. The strongest correlation (Spearman ρ = 0.71; p < 0.001) and highest area under the ROC curve (AUROC) for detecting lymphoma activity were observed for nIC normalized to inferior vena cava enhancement (AUROC = 0.866). The latter provided sensitivity, specificity, and diagnostic accuracy of 87 %, 75 %, and 80 %, respectively, at a threshold of 0.20. ROC analysis for AV (AUROC = 0.834) and aIC (AUROC = 0.853) yielded similar results.

CONCLUSION

 In malignant lymphomas, there is a significant correlation between metabolic activity as assessed by FDG-PET/CT and iodine concentration as assessed by SDCT. Iodine concentration shows promising diagnostic performance for detecting lymphoma activity and may represent a potential imaging biomarker.

KEY POINTS

  · Iodine concentration correlates significantly with SUVmax in lymphoma patients. · Iodine concentration may represent a potential imaging biomarker for detecting lymphoma activity. · Normalization of iodine concentration improves diagnostic performance of iodine concentration.

CITATION FORMAT

· Gehling K, Mokry T, Do TD et al. Dual-Layer Spectral Detector CT in Comparison with FDG-PET/CT for the Assessment of Lymphoma Activity. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1735-3477.

Five years of thoracic endoscopy unit activity on lung cancer staging: how teamwork can improve the outcomes

Background

Regarding the staging of mediastinal lymph nodes before lung cancer surgery, Endobronchial Ultrasound Transbronchial Needle Aspirations (EBUS-TBNA) have proven to be highly sensitive and specific as well as safe. Endoscopic Ultrasound Fine Needle Aspirations (EUS-FNA) plays an important role in the diagnosis and staging of thoracic diseases, including lung cancer. In this study we analysed all patients underwent endoscopic procedures in our endoscopic mediastinal ultrasound unit.

Methods

Between January 2013 and February 2018, we performed a total of 929 endoscopic procedures, 432 EBUS-TBNA and 497 EUS-FNA. Biopsy was performed at the following mediastinal sites: station 7 in 642 cases, at stations 8 and 9 in 211 cases; at station 3P and 4L in 27 and 114 cases respectively; with EUS we were able to perform biopsy at station 5 in 52 cases.

Results

A total of 841 patients showed a diagnosis of cancer: non-small cell lung cancer (NSCLC) in 645 patients, SCLC in 190 patients, neuroendocrine tumour in 5 patients and one patient with mesothelioma. 88 patients were negative for cancer. In terms of sensitivity, specificity and accuracy, the association between EUS-FNAb and EBUS-TBNAb showed a better quality on diagnosis compared to single procedures. EUS-FNA and EBUS-TBNA are safe, feasible, and highly sensitive techniques.

Conclusions

An endoscopic mediastinal ultrasound unit allows to perform a higher number of endoscopic procedures and improved the sensitivity and the accuracy of the minimally invasive hilar-mediastinal staging.

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.

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.

Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the ¹⁸F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.

Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).

Conclusions: The RM based on ¹⁸F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions.

Improving accuracy of 18F-fluorodeoxyglucose PET computed tomography to diagnose nodal involvement in non-small cell lung cancer: utility of using various predictive models

PURPOSE

To determine predictive models (PM) that could improve the accuracy for identifying metastatic regional nodes in non-small cell lung cancer based on both PET and CT findings seen on 18F-FDG PET CT.

METHODS

Three hundred thirty-nine biopsy-proven NSCLC patients who underwent surgical resection and had a staging 18F-FDG PET CT were enrolled. PET parameters obtained were (1) presence of visual PET positive nodes, (2) SUVmax of nodes (NSUV), (3) ratio of node to aorta SUVmax (N/A ratio) and (4) ratio of node to primary tumour SUVmax (N/T ratio). CT parameters obtained were (1) short-axis diameter and (2) Hounsfield units (HU) of PET-positive nodes. PET and CT parameters were correlated with nodal histopathology to find out the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and overall accuracy. Different PM combining these parameters were devised and the incremental improvement in accuracy was determined.

RESULTS

Visual PET positivity showed sensitivity, specificity, PPV, NPV and accuracy of 72.4, 76.1, 30.1, 95.1 and 75.6, respectively. PM2 which combined visual PET positivity, NSUV and HU appears more clinically relevant and showed sensitivity, specificity, PPV, NPV and accuracy of 53.5, 96.5, 68.9, 93.6 and 91.2, respectively. PM6 which combined visual PET positivity, NSUV, N/A ratio and HU showed the maximum PPV (80.0%), specificity (98.3%) and accuracy of (91.9%).

CONCLUSION

PM combining parameters like nodal SUVmax, N/A ratio, N/T ratio and HU values have shown to improve the PPV, specificity and overall accuracy of 18FDG PET CT in the preoperative diagnosis of nodal metastases.

Early SUVmax is the best predictor of axillary lymph node metastasis in stage III breast cancers

Background

Although fluorine-18-labeled 2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission/computed tomography (PET/CT) imaging has been investigated for its ability to evaluate lymph node metastasis of breast cancer, few comparative analyses have evaluated the preoperative and postoperative regional lymph node metastasis of breast cancer by dual-phase imaging, especially in patients with stage III (N2) disease.

Methods

The clinical, pathological, and imaging data of 40 patients with stage III (N2) breast cancer were retrospectively analyzed. All patients underwent dual-time point ¹⁸F-FDG imaging before surgery and postoperative pathology examinations were obtained. The short-axis lymph node diameter was measured, and the maximum standardized uptake value (SUV_max) and the percentage difference of SUV_max between dual-phase (ΔSUV_max) were calculated to compare metastatic and non-metastatic lymph nodes on dual-time point images.

Results

A total of 398 axillary lymph nodes were inspected, and 209 lymph nodes were matched with those on PET/CT images, including 97 metastatic and 112 non-metastatic lymph nodes. The SUV_max values were significantly different between metastatic and non-metastatic lymph nodes, in both the early and delayed scans (P<0.001). For metastatic lymph nodes, the SUV_max value on the delayed scan (6.17±2.62) was significantly higher compared with the early scan (5.45±1.35; ΔSUV_max =0.08±0.21, P<0.001). Moreover, the SUV_max values were not significantly different between the delayed (2.82±0.91) and early scans (2.79±0.72; ΔSUV_max=-0.00±0.11, P=0.77). The short diameters were not significantly different between metastatic and non-metastatic lymph nodes (P=0.12), and the SUV_max values of metastatic lymph nodes with short diameters of >4.00 and ≤6.00 mm were not significantly different between the early and delayed scans (P=0.06). However, the SUV_max values of metastatic lymph nodes with short diameters of >6.00 and ≤8.00 mm (7.11±0.19 vs. 5.96±0.08) and short diameters of >8.00 and ≤10.00 mm (10.76±0.35 vs. 6.82±0.50) were higher on the delayed scan versus the early scan, respectively (P<0.01 for each comparison). The difference between the ΔSUV_max values among the three subgroups was statistically significant (F=78.98, P<0.001).The receiver operating characteristic (ROC) curve analysis of the lymph nodes showed that the area under the curve (AUC) of the early and delayed PET/CT scans was 0.961 (0.925-0.983, P=0.013) and 0.897 (0.847-0.934, P=0.022), respectively. The ROC curves of the early and delayed scans were also significantly different (z=4.46, P<0.001). AUC of the ΔSUV_max for the early scan was significantly lower compared with delayed scans (z=8.95 vs. 9.13, respectively; P<0.001).

Conclusions

Dual-time point ¹⁸F-FDG PET imaging significantly improved the prediction and detection of axillary lymph node metastasis, compared with prediction based on size of lymph node alone, in patients with stage III breast cancer. We found that lymph nodes with continuously increased SUV_max values tended to show metastasis, and early SUV_max assessment offers the best capacity for prediction of axillary lymph node metastasis.

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.

Is FDG-PET/CT used correctly in the combined approach for nodal staging in NSCLC patients?

Background

The most widely accepted approach nowadays in nodal staging of non-small cell lung cancer (NSCLC) is the combined use of 18-Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) and endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). However, this approach may not be sufficient, especially for early stages.

Aims

Our aim was to assess whether more satisfactory results can be obtained with standardized uptake value maximum lymph node/standardized uptake value mean mediastinal blood pool (SUVmax LN/SUVmean MBP), SUVmax LN/Primary tumor, or a novel cut-off value to SUVmax in this special group.

Subjects and Methods

Patients with diagnosed NSCLC and underwent FDG-PET/CT were reviewed retrospectively. 168 LNs of 52 early stage NSCLC patients were evaluated. The LNs identified in surgery/pathology reports were found in the FDG-PET/CT images. Anatomic and metabolic parameters were measured. Statistical analysis was performed by using of MedCalc Statistical Software.

Results

Regardless of LNs size; sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SUVmax >2.5 were 91.5%, 65.9%, 58.2%, and 95.1%, respectively. Optimum cut-off value of SUVmax was >4.0. Sensitivity, specificity, PPV, and NPV were found as 81.0%, 90.0%, 81.0%, and 90.0% respectively. Optimum cut-off value of SUVmax LN/SUVmean MBP was >1.71. Sensitivity, specificity, PPV, and NPV were found as 94.7%, 80.0%, 71.1%, and 96.7%, respectively. Optimum cut-off value of SUVmax LN/Primary tumor was >0.28. Sensitivity, specificity, PPV, and NPV were found as 81.1%, 85.1%, 72.9% and 90.1%, respectively.

Conclusion

SUVmax LN/SUVmean MBP >1.71 has higher PPV than currently used, with similar NPV and sensitivity. This can provide increase in the accuracy of combined approach. In this way, faster nodal staging/treatment decisions, cost savings for healthcare system and time saving of medical professionals can be obtained.

Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images

OBJECTIVE. The purpose of this study was to assess, by analyzing features of the primary tumor with ¹⁸F-FDG PET, the utility of deep machine learning with a convolutional neural network (CNN) in predicting the potential of newly diagnosed non-small cell lung cancer (NSCLC) to metastasize to lymph nodes or distant sites. MATERIALS AND METHODS. Consecutively registered patients with newly diagnosed, untreated NSCLC were retrospectively included in a single-center study. PET images were segmented with local image features extraction software, and data were used for CNN training and validation after data augmentation strategies were used. The standard of reference for designation of N category was invasive lymph node sampling or 6-month follow-up imaging. Distant metastases developing during the study follow-up period were assessed by imaging (CT or PET/CT), in tissue obtained from new suspected sites of disease, and according to the treating oncologist's designation. RESULTS. A total of 264 patients with NSCLC participated in follow-up for a median of 25.2 months (range, 6-43 months). N category designations were available for 223 of 264 (84.5%) patients, and M category for all 264. The sensitivity, specificity, and accuracy of CNN for predicting node positivity were 0.74 ± 0.32, 0.84 ± 0.16, and 0.80 ± 0.17. The corresponding values for predicting distant metastases were 0.45 ± 0.08, 0.79 ± 0.06, and 0.63 ± 0.05. CONCLUSION. This study showed that using a CNN to analyze segmented PET images of patients with previously untreated NSCLC can yield moderately high accuracy for designation of N category, although this may be insufficient to preclude invasive lymph node sampling. The sensitivity of the CNN in predicting distant metastases is fairly poor, although specificity is moderately high.

Quantitative computed tomography texture analysis: can it improve diagnostic accuracy to differentiate malignant lymph nodes?

Background and objective

Mediastinal lymph node (LN) staging in individuals with non-small-cell lung cancer plays an important role in staging and treatment planning. This study aimed to assess the accuracy of computed tomography (CT) texture analysis (CTTA) in differentiating benign and malignant mediastinal LNs.

Methods

Pathologically confirmed malignant and benign mediastinal LN samples, obtained using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), were retrospectively reviewed, in addition to chest CT and 18-fluorodeoxyglucose (FDG) uptake positron emission tomography (PET) data. For each LN, CTTA was performed using “AVIEW” software (Coreline Soft, Republic of Korea) by drawing a region of interest.

Results

A total of 132 LNs from 80 patients were included and classified into two groups according to pathology results: malignant (n = 61) and benign (n = 71). In EBUS, size > 1 cm, round shape, heterogeneous echogenicity, and presence of coagulation necrosis sign were more prevalent in malignant than in benign LNs; length was the only feature that distinguished the two groups. Among CTTA features, compactness and normalized standard deviation (SD) showed differences between the two groups. The ability to distinguish malignant LNs was higher using high standard uptake value (SUV) on FDG PET/CT (SUV_max ≥ 5) and normalized SD on CTTA (area under the receiver operating characteristic curve 0.739 versus 0.742, respectively); however, normalized SD demonstrated very low sensitivity despite high specificity.

Conclusions

CTTA may be helpful in distinguishing between benign and malignant LNs; however, the diagnostic value was not high. Therefore, integrated evaluation with other imaging modalities is needed.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0214-8) contains supplementary material, which is available to authorized users.

An exceptional group of non-small cell lung cancer difficult to diagnose: Evaluation of lipid-poor adrenal lesions

In some non-small cell lung cancer (NSCLC) patients, lipid-poor adrenal adenomas cannot be adequately differentiated from metastases using imaging methods. Invasive diagnostic procedures also have a low negative predictive value (NPV) in such cases. The current study aims to establish a specific and clinically practical metabolic parameter for lipid-poor adrenal lesions (ALs) in NSCLC patients. This diagnostic approach may prevent unnecessary abdominal enhanced computed tomography (CT), magnetic resonance imaging, or invasive diagnostic procedures. Sixty-four NSCLC patients with 69 lipid-poor ALs and 28 control patients with 30 benign lipid-poor ALs, who underwent FDG-PET/CT, were retrospectively reviewed. Two morphological and four metabolic parameters were analyzed in FDG-PET/CT images of NSCLC and control patients. Baseline and post-chemotherapy images of 64 NSCLC patients were re-evaluated according to the PERCIST 1.0. In cases where ALs could not be differentiated, follow-up FDG-PET/CT images were re-examined. The receiver operating characteristic (ROC) curve method was used for the evaluation of diagnostic parameters. Out of 69 ALs, 39 were determined as metastatic lesions (adrenal metastasis), while 30 lesions were considered non-metastatic (adrenal adenomas). The mean attenuation value, SUVmax AL/SUVmax primary tumor, SUVmax, SUVmax AL/liver, and SUVmax AL/SUVmean liver were significantly different between metastatic and benign ALs from NSCLC patients. The SUVmax AL/SUVmean liver ≥1.81 had the best positive (PPV, 94.3%) and negative (NPV, 82.4%) predictive values, and the highest specificity (93.3%), sensitivity (84.6%) and accuracy (86.9%). Lipid-poor ALs with SUVmax AL/SUVmean liver ≥1.81 can be accepted as malignant in NSCLC. However, if SUVmax AL/SUVmean liver is <1.81, a pathologic examination is required. Utilizing this cut-off value to decide on adrenal core biopsy may prevent its unnecessary use. Moreover, this diagnostic approach can save time and reduce the healthcare costs.

Radiomics in Oncological PET/CT: Clinical Applications

18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

FDG PET/CT Overcomes Discordance Between Clinical and Pathologic TNM Classification of Small-size Primary Lung Cancer: Influence on Postoperative Prognosis

INTRODUCTION

We aimed to determine the concordance between the clinical stage (c-stage) and pathologic stage (p-stage) for patients with small-size lung cancer. Additionally we searched for prognostic factors other than the TNM stage.

PATIENTS AND METHODS

We retrospectively reviewed the preoperative multidetector computed tomography (CT) and positron emission tomography/CT reports, surgical records, and pathologic reports of patients with primary lung cancer ≤ 3 cm. The Union for International Cancer Control TNM seventh edition classification of c-stage and p-stage were compared. The tumors were classified into multiple subgroups by concordance or discordance between the c-stage and p-stage. Disease-free survival (DFS) was assessed using survival analysis to assess the tumor characteristics that were predictive of prognosis.

RESULTS

A total of 289 surgically resected primary lung cancers were evaluated. The concordance between c-stage and p-stage was 65.4%, with moderate reproducibility (kappa coefficient, 0.467). The upstaging rate from c-stage I to p-stage II-IV was 9.4%, and these patients had significantly worse DFS than those with a concordant stage I classification (P < .001). The main reason for upstaging was an underestimation of metastases to the hilar lymph nodes (n = 7) or mediastinal lymph nodes (n = 11). A multivariate Cox proportional hazards model showed that the significant predictive factors for DFS were p-stage (hazard ratio, 1.342; P = .003) and maximum standardized uptake value on positron emission tomography/CT (hazard ratio, 12.162; P = .001).

CONCLUSION

The concordance rate between c-stage and p-stage for small primary lung cancers had moderate reproducibility. Discordance between c-stage I and p-stage II-IV significantly affected DFS. The maximum standardized uptake value of the primary lesion was an independent prognostic factor, and combining it with c-stage might improve the prediction of therapeutic outcomes.