Quantitative Volumetric CT-Histogram Analysis in N-Staging of 18F-FDG–Equivocal Patients with Lung Cancer

CT features and histogram analysis of non-contrast images for differentiating malignant and benign mediastinal lymph nodes in Non-Small Cell Lung Cancer (NSCLC)

OBJECTIVE

To evaluate the diagnostic value of CT features and histogram analysis in distinguishing between malignant and benign mediastinal lymph nodes in patients with non-small cell lung cancer (NSCLC).

METHOD

This retrospective study analyzed non-contrast chest CT images from 40 NSCLC patients, comprising 80 pathology-proven mediastinal lymph nodes (46 benign, 34 metastasis). Morphologic features, including size, shape, margins, and internal composition, were independently assessed by two radiologists. Histogram analysis was conducted using the Synapse Vincent system with six parameters: mean attenuation, mean positive pixel (MPP), standard deviation (SD), skewness, kurtosis, and entropy. Statistical analysis included the Mann-Whitney test for continuous data, Fisher's exact test for categorical data, and receiver-operating characteristic (ROC) curve analysis to assess diagnostic accuracy, with statistical significance set at p < 0.05.

RESULTS

Malignant lymph nodes demonstrated significantly larger sizes (p < 0.001), ill-defined margins (p = 0.024), irregular shapes (p < 0.001), and the presence of necrotic areas (p < 0.001). A nodal size cutoff of 13.0 mm and volume of 1.632 ml were strongly associated with malignancy, yielding high diagnostic accuracy with sensitivities of 70.6% and 73.5% and specificities of 95.7% and 87.0%, respectively. Significant differences were observed between benign and malignant lymph nodes in several CT histogram parameters, including mean attenuation (p = 0.004), skewness (p = 0.041), kurtosis (p = 0.005), and entropy (p < 0.001). The integrating all CT histogram parameters yielded an area under the curve (AUC) of 0.870 for differentiating between benign and malignant lymph nodes.

CONCLUSION

The combination of morphologic CT features and CT histogram analysis offers a robust method for differentiating malignant from benign mediastinal lymph nodes in NSCLC patients, potentially enhancing diagnostic accuracy and informing treatment strategies.

Predicting the immune therapy response of advanced non-small cell lung cancer based on primary tumor and lymph node radiomics features

Objective

To identify imaging biomarkers of primary tumors and lymph nodes in patients with stage III-IV non-small cell lung cancer (NSCLC) and assess their predictive ability for treatment response (response vs. non-response) to immune checkpoint inhibitors (ICIs) after 6 months.

Methods

Retrospective analysis of 83 NSCLC patients treated with ICIs. Quantitative imaging features of the maximum primary lung tumors and lymph nodes on contrast-enhanced CT imaging were extracted at baseline (time point 0, TP0) and after 2-3 cycles of immunotherapy (time point 1, TP1). Delta-radiomics features (delta-RFs) were defined as the net changes in radiomics features (RFs) between TP0 and TP1. Interobserver interclass coefficient (ICC) and Pearson correlation analyses were applied for feature selection, and logistic regression (LR) was used to build a model for predicting treatment response.

Results

Four and five important delta-RFs were selected to construct the nodal and tumor models, respectively. Δ Tumor diameter was used for constructing the clinical prediction model. The predictive efficacy of the nodal model for the treatment response status was higher than that of the tumor and clinical models. In the training set, the AUC values for the three models were 0.96 (95% CI = 0.90-1.00), 0.86 (95% CI = 0.76-0.95), and 0.82 (95% CI = 0.71-0.93), respectively. In the validation set, the AUC values were 0.94 (95% CI = 0.85-1.00), 0.77 (95% CI = 0.56-0.98), and 0.74 (95% CI = 0.48-1.00), respectively.

Conclusion

The nodal model based on delta-RFs performed well in distinguishing responders from non-responders and could identify patients more likely to benefit from immunotherapy. Finally, the nodal model exhibited a higher classification performance than the tumor model.

Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition

PURPOSE

This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma.

METHODS

A total of 123 cases devoid of lymphoma underwent whole-body 18F-FDG-PET/CT scans to facilitate the development of an advanced SAU2Net model, which combines the advantages of U2Net and attention mechanism. This model integrated inputs from simulated 1/2-dose (0.07 mCi/kg) PET acquisition across multiple slices to generate an estimated standard dose (0.14 mCi/kg) PET scan. Additional 39 cases with confirmed lymphoma pathology were utilized to evaluate the model's clinical performance. Assessment criteria encompassed peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), a 5-point Likert scale rated by two experienced physicians, SUV features, image noise in the liver, and contrast-to-noise ratio (CNR). Diagnostic outcomes, including lesion numbers and Deauville score, were also compared.

RESULTS

Images enhanced by the proposed DL method exhibited superior image quality (P < 0.001) in comparison to low-dose acquisition. Moreover, they illustrated equivalent image quality in terms of subjective image analysis and lesion maximum standardized uptake value (SUVmax) as compared to the standard acquisition method. A linear regression model with y = 1.017x + 0.110 (R 2 = 1.00 ${R^2} = \;1.00$ ) can be established between the enhanced scans and the standard acquisition for lesion SUVmax. With enhancement, increased signal-to-noise ratio (SNR), CNR, and reduced image noise were observed, surpassing those of the standard acquisition. DL-enhanced PET images got diagnostic results essentially equavalent to standard PET images according to two experienced readers.

CONCLUSION

The proposed DL method could facilitate a 50% reduction in PET imaging duration for lymphoma patients, while concurrently preserving image quality and diagnostic accuracy.

The Effect of Hounsfield Unit Value on the Differentiation of Malignant/Benign Mediastinal Lymphadenopathy and Masses Diagnosed by Endobronchial Ultrasonography

Aim

In cases where standardized maximum uptake (SUVmax) values in positron emission tomography (PET-CT) were not sufficient to differentiate mediastinal lymphadenopathy and masses from malignant or benign, the contribution of Hounsfield unit (HU) values in thorax computed tomography to the diagnosis was evaluated.

Material Method

The study was conducted by evaluating the data of 182 patients between 2019 and 2023. HU values on non-contrast thorax computed tomography and PET-CT SUVmax values of biopsied masses and lymph nodes were compared with histopathological diagnoses.

Results

Patients, 58 females (31.9%) and 124 males (68.1%), who underwent EBUS were included in the study. Biopsies were taken from 233 stations (199 lymph nodes, 34 masses) from 182 patients. A total of 135 of the biopsies taken from 233 stations were histopathologically malignant and 98 were benign. While PET-CT SUVmax values of cases with benign histopathology were 4.5 ± 3.5, it was 7.6 ± 4.2 in patients with malignant pathology (p<0.05). The HU value on non-contrast thorax tomography in patients with benign histopathology was 43.1 ± 15.7, and in patients with malignant histopathology it was 40.5 ± 13.7 (p>0.05). When HU was compared according to lung cancer type, it was found to be significantly higher in non-small cell lung cancer (p=0.035). A weak (r=0.182) positive and significant relationship (p<0.01) was found between PET-CT values and HU values in thorax computed tomography.

Conclusion

While positron emission tomography maintains its importance in the differentiation of mediastinal lymphadenopathy and masses from malignant to non-malignant, it was concluded that HU values in computed tomography are not sufficient to distinguish malignant/non-malignant.

[Lesions of the visceral mediastinum]

BACKGROUND

The visceral or middle mediastinum contains nonvascular (trachea, carina, esophagus, and lymph nodes) and vascular structures (heart, ascending aorta, aortic arch, descending aorta, superior vena cava, intrapericardial pulmonary arteries, thoracic duct).

OBJECTIVES

The various pathologies of the visceral mediastinum and imaging features are presented.

MATERIALS AND METHODS

Plain film radiography shows the gross anatomy and allows visualization of larger pathologies. However, for detailed anatomic and structural classification more sophisticated imaging techniques are required. Especially computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are well suited for structural and functional assessment of mediastinal lesions.

CONCLUSION

This article summarizes the major pathologies of the visceral mediastinum.

Mask R-CNN assisted 2.5D object detection pipeline of 68Ga-PSMA-11 PET/CT-positive metastatic pelvic lymph node after radical prostatectomy from solely CT imaging

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) is a molecular and functional imaging modality with better restaging accuracy over conventional imaging for detecting prostate cancer in men suspected of lymph node (LN) progression after definitive therapy. However, the availability of PSMA PET/CT is limited in both low-resource settings and for repeating imaging surveillance. In contrast, CT is widely available, cost-effective, and routinely performed as part of patient follow-up or radiotherapy workflow. Compared with the molecular activities, the morphological and texture changes of subclinical LNs in CT are subtle, making manual detection of positive LNs infeasible. Instead, we harness the power of artificial intelligence for automated LN detection on CT. We examined ⁶⁸Ga-PSMA-11 PET/CT images from 88 patients (including 739 PSMA PET/CT-positive pelvic LNs) who experienced a biochemical recurrence after radical prostatectomy and presented for salvage radiotherapy with prostate-specific antigen < 1 ng/mL. Scans were divided into a training set (nPatient = 52, nNode = 400), a validation set (nPatient = 18, nNode = 143), and a test set (nPatient = 18, nNodes = 196). Using PSMA PET/CT as the ground truth and consensus pelvic LN clinical target volumes as search regions, a 2.5-dimensional (2.5D) Mask R-CNN based object detection framework was trained. The entire framework contained whole slice imaging pretraining, masked-out region fine-tuning, prediction post-processing, and "window bagging". Following an additional preprocessing step-pelvic LN clinical target volume extraction, our pipeline located positive pelvic LNs solely based on CT scans. Our pipeline could achieve a sensitivity of 83.351%, specificity of 58.621% out of 196 positive pelvic LNs from 18 patients in the test set, of which most of the false positives can be post-removable by radiologists. Our tool may aid CT-based detection of pelvic LN metastasis and triage patients most unlikely to benefit from the PSMA PET/CT scan.

Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning

Background

We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference.

Methods

This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists.

Results

All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75−0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit.

Conclusions

Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-022-00296-8.

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.

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.

A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer

Objectives

To develop and validate a PET/CT nomogram for preoperative estimation of lymph node (LN) staging in patients with non-small cell lung cancer (NSCLC).

Methods

A total of 263 pathologically confirmed LNs from 124 patients with NCSLC were retrospectively analyzed. Positron-emission tomography/computed tomography (PET/CT) examination was performed before treatment according to the clinical schedule. In the training cohort (N = 185), malignancy-related features, such as SUVmax, short-axis diameter (SAD), and CT radiomics features, were extracted from the regions of LN based on the PET/CT scan. The Minimum-Redundancy Maximum-Relevance (mRMR) algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model were used for feature selection and radiomics score building. The radiomics score (Rad-Score) and SUVmax were incorporated in a PET/CT nomogram using the multivariable logistic regression analysis. The performance of the proposed model was evaluated with discrimination, calibration, and clinical application in an independent testing cohort (N = 78).

Results

The radiomics scores consisting of 14 selected features were significantly associated with LN status for both training cohort with AUC of 0.849 (95% confidence interval (CI), 0.796–0.903) and testing cohort with AUC of 0.828 (95% CI, 0.782–0.919). The PET/CT nomogram incorporating radiomics score and SUVmax showed moderate improvement of the efficiency with AUC of 0.881 (95% CI, 0.834–0.928) in the training cohort and AUC of 0.872 (95% CI, 0.797–0.946) in the testing cohort. The decision curve analysis indicated that the PET/CT nomogram was clinically useful.

Conclusion

The PET/CT nomogram, which incorporates Rad-Score and SUVmax, can improve the diagnostic performance of LN metastasis.

Key Points

• The PET/CT nomogram (Int-Score) based on lymph node (LN) PET/CT images can reliably predict LN status in NSCLC.

• Int-Score is a relatively objective diagnostic method, which can play an auxiliary role in the process of clinicians making treatment decisions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07624-9.

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients

PURPOSE

In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs).

METHODS

Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling.

RESULTS

Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter.

CONCLUSION

The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.

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.

Impact of FDG-PET on the Detection of Patients with Lung Cancer at High Risk for ILD

BACKGROUND/AIM

Idiopathic pulmonary fibrosis IPF is a type of interstitial lung disease (ILD) with poor prognosis. Lung cancer (LC) is a frequent complication in IPF, where all therapeutic options are potential triggers for acute exacerbation of IPF.

PATIENTS AND METHODS

Patients with 2-deoxy-2-fluoro-D-glucose-positron emission tomography/computer tomography (FDG-PET/CT) results before lobectomy for LC with and without (n=10 each) signs of ILD in initial imaging and after-care CT were retrospectively analyzed. FDG uptake was calculated as the maximum standardized uptake value (SUV_max) in the lung periphery divided by the SUV_max of the mediastinal blood pool (rSUV_max). Regional increase of fibrosis and ground-glass features in lobe-based CT analysis was used as standard reference.

RESULTS

Patients with LC with ILD presented a significantly higher rSUV_max of 0.57 compared to patients without ILD with rSUV_max 0.47 (p<0.001).

CONCLUSION

rSUV_max seems to be a valuable imaging surrogate in predicting patients with LC with increased risk for progressive ILD associated with thoracic surgery.

Impact of Computer-Aided CT and PET Analysis on Non-invasive T Staging in Patients with Lung Cancer and Atelectasis

PURPOSE

Tumor delineation within an atelectasis in lung cancer patients is not always accurate. When T staging is done by integrated 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG)-positron emission tomography (PET)/X-ray computer tomography (CT), tumors of neuroendocrine differentiation and slowly growing tumors can present with reduced FDG uptake, thus aggravating an exact T staging. In order to further exhaust information derived from [¹⁸F]FDG-PET/CT, we evaluated the impact of CT density and maximum standardized uptake value (SUVmax) for the classification of different tumor subtypes within a surrounding atelectasis, as well as possible cutoff values for the differentiation between the primary tumor and atelectatic lung tissue.

PROCEDURES

Seventy-two patients with histologically proven lung cancer and adjacent atelectasis were investigated. Non-contrast-enhanced [¹⁸F]FDG-PET/CT was performed within 2 weeks before surgery/biopsy. Boundaries of the primary within the atelectasis were determined visually on the basis of [¹⁸F]FDG uptake; CT density was quantified manually within each primary and each atelectasis.

RESULTS

CT density of the primary (36.4 Hounsfield units (HU) ± 6.2) was significantly higher compared to that of atelectatic lung (24.3 HU ± 8.3; p < 0.01), irrespective of the histological subtype. The discrimination between different malignant tumors using density analysis failed. SUVmax was increased in squamous cell carcinomas compared to adenocarcinomas. Irrespective of the malignant subtype, a possible cutoff value of 24 HU may help to exclude the presence of a primary in lesions below 24 HU, whereas a density above a threshold of 40 HU can help to exclude atelectatic lung.

CONCLUSION

Density measurements in patients with lung cancer and surrounding atelectasis may help to delineate the primary tumor, irrespective of the specific lung cancer subtype. This could improve T staging and radiation treatment planning (RTP) without additional application of a contrast agent in CT, or an additional magnetic resonance imaging (MRI), even in cases of lung tumors of neuroendocrine differentiation or in slowly growing tumors with less avidity to [¹⁸F]FDG.

Role of CT Density in PET/CT-Based Assessment of Lymphoma

PURPOSE

In patients with Hodgkin (HL) and non-Hodgkin lymphoma (NHL), primary staging, as well as intermediate and late response assessment, is often performed by integrated 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography/X-ray computed tomography (PET/CT). The purpose of this analysis was to evaluate if findings in patients with histopathologically proven HL or NHL might correlate with semi-automated density measurements of target lesions (TLs) in the CT component of the integrated PET/CT examination.

PROCEDURES

After approval by the institutional review board, 176 lymph nodes (LN) in 90 PET/CT examinations of 90 patients were retrospectively analyzed (HL, 108 TLs out of 55 patients; NHL, 68 TLs out of 35 patients). PET/CT was performed for reasons of primary staging, response evaluation as interim PET, or as final examination after therapy, according to the clinical schedule. Analyses of TLs were performed on the basis of tracer uptake (SUV) 60 min after tracer injection and volumetric CT histogram analysis in non-contrast-enhanced CT.

RESULTS

All patients were diagnosed with HL or NHL in a pretreatment biopsy. Prior to therapy induction, staging of all patients was performed using contrast-enhanced CT of the neck to the pelvis, or by [¹⁸F]FDG PET/CT. Of the 176 TLs, 119 were classified as malignant, and 57 were benign. Malignant TLs had significantly higher CT density values compared to benign (p < 0.01).

CONCLUSION

Density measurements of TLs in patients with HL and NHL correlate with the dignity of TLs and might therefore serve as a complementary surrogate parameter for the differentiation between malignant and benign TLs. A possible density threshold in clinical routine might be a 20-Hounsfield units (HU) cutoff value to rule out benignancy in TLs that are above the 20-HU threshold.

Diagnostic Imaging and Newer Modalities for Thoracic Diseases: PET/Computed Tomographic Imaging and Endobronchial Ultrasound for Staging and Its Implication for Lung Cancer

Modalities to detect and characterize lung cancer are generally divided into those that are invasive [endobronchial ultrasound (EBUS), esophageal ultrasound (EUS), and electromagnetic navigational bronchoscopy (ENMB)] versus noninvasive [chest radiography (CXR), computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI)]. This chapter describes these modalities, the literature supporting their use, and delineates what tests to use to best evaluate the patient with lung cancer.

Radiomic Analysis using Density Threshold for FDG-PET/CT-Based N-Staging in Lung Cancer Patients

PURPOSE

Mediastinal nodal (N)-staging done by integrated 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography/x-ray computed tomography (PET/CT) in lung cancer patients is not always accurate. In order to reduce the need for invasive staging procedures, additional surrogate parameters for the detection of malignant lymph node infiltration would be helpful. The purpose of this study was to evaluate if radiomic semi-automated density profiling in mediastinal lymph nodes can improve preclinical N-staging, irrespective of the specific lung cancer entity.

PROCEDURES

This retrospective study was approved by the institutional review board. Two hundred forty-eight histologically proven lymph nodes in 122 lung cancer patients were investigated. In malignantly infiltrated lymph nodes, the specific lung cancer entity was histologically classified; benign lymph nodes were histologically classified as benign. Non-contrast enhanced [¹⁸F]FDG-PET/CT was performed before surgery/biopsy. Lymph node analyses were performed on the basis of FDG uptake and volumetric CT histogram analysis for metric lymph node sampling.

RESULTS

Of the 248 lymph nodes, 118 were benign, 130 malignant. Malignant lymph nodes had a significantly higher median CT density (32.4 Hounsfield units (HU) (min 5.4/max 77.5 HU)) compared to benign lymph nodes (9.3 HU (min -49.5/max 60.4 HU, p < 0.05), irrespective of the histological subtype. The discrimination between different malignant tumour subtypes by means of volumetric density analysis failed. Irrespective of the malignant subtype, a possible cutoff value of 20 HU may help differentiate between benign and malignant lymph nodes.

CONCLUSION

Density measurements in unclear mediastinal and hilar lymph nodes with equivocal FDG uptake in PET might serve as a possible surrogate parameter for N-staging in lung cancer patients, irrespective of the specific lung cancer subtype. This could also help to find possible high yield targets in cases where invasive lymph node staging is necessary.

Predicting the nodal status in gastric cancers: The role of apparent diffusion coefficient histogram characteristic analysis

PURPOSE

To explore the application of histogram analysis in preoperative T and N staging of gastric cancers, with a focus on characteristic parameters of apparent diffusion coefficient (ADC) maps.

MATERIALS AND METHODS

Eighty-seven patients with gastric cancers underwent diffusion weighted magnetic resonance imaging (b=0, 1000s/mm2), which generated ADC maps. Whole-volume histogram analysis was performed on ADC maps and 7 characteristic parameters were obtained. All those patients underwent surgery and postoperative pathologic T and N stages were determined.

RESULTS

Four parameters, including skew, kurtosis, s-sDav and sample number, showed significant differences among gastric cancers at different T and N stages. Most parameters correlated with T and N stages significantly and worked in differentiating gastric cancers at different T or N stages. Especially skew yielded a sensitivity of 0.758, a specificity of 0.810, and an area under the curve (AUC) of 0.802 for differentiating gastric cancers with and without lymph node metastasis (P<0.001). All the parameters, except AUClow, showed good or excellent inter-observer agreement with intra-class correlation coefficients ranging from 0.710 to 0.991.

CONCLUSION

Characteristic parameters derived from whole-volume ADC histogram analysis could help assessing preoperative T and N stages of gastric cancers.

Diagnostic Imaging and Newer Modalities for Thoracic Diseases: PET/Computed Tomographic Imaging and Endobronchial Ultrasound for Staging and Its Implication for Lung Cancer

Modalities to detect and characterize lung cancer are generally divided into those that are invasive [endobronchial ultrasound (EBUS), esophageal ultrasound (EUS), and electromagnetic navigational bronchoscopy (ENMB)] versus noninvasive [chest radiography (CXR), computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI)]. This chapter describes these modalities, the literature supporting their use, and delineates what tests to use to best evaluate the patient with lung cancer.

Three-dimensional substructure measurements for the differential diagnosis of ground glass nodules

BACKGROUND

We analyzed the differences between maximum and peak computed tomography (CT) numbers (M-P), respectively representing the densities of the solid center and the main periphery of ground-glass nodules (GGNs), and the average change in M-P velocity (V(M-P)) during follow-up to differentiate between pre-invasive (PIA) and invasive adenocarcinoma (IAC).

METHODS

Data of 102 patients were retrospectively collected and analyzed in our study including 43 PIAs and 59 IACs. Diameters, total volumes, and the maximum and peak CT numbers in CT number histograms were measured and followed for at least 3 months. This study was registered retrospectively.

RESULTS

The M-P values for IACs were higher than those for PIAs (p = 0.001), with an area under the curve (AUC) of 0.810 and a threshold of 489.5 Hounsfield units (HU) in ROC analysis. The V(M-P) values for IACs were smaller than those for PIAs (p = 0.04), with an AUC of 0.805 and a threshold of 11.01 HU/day.

CONCLUSIONS

M-P and V(M-P) values may help distinguish IACs from PIAs by representing the changes in the sub-structural densities of GGNs during follow-up.

Increased x-ray attenuation in malignant vs. benign mediastinal nodes in an orthotopic model of lung cancer

PURPOSE

Staging of lung cancer is typically performed with fluorodeoxyglucose-positron emission tomography-computed tomography (FDG-PET/CT); however, false positive PET scans can occur due to inflammatory disease. The CT scan is used for anatomic registration and attenuation correction. Herein, we evaluated x-ray attenuation (XRA) within nodes on CT and correlated this with the presence of malignancy in an orthotopic lung cancer model in rats.

METHODS

1×10⁶ NCI-H460 cells were injected transthoracically in six National Institutes of Health nude rats and six animals served as controls. After two weeks, animals were sacrificed; lymph nodes were extracted and scanned with a micro-CT to determine their XRA prior to histologic analysis.

RESULTS

Median CT density in malignant lymph nodes (n=20) was significantly higher than benign lymph nodes (n=12; P = 0.018). Short-axis diameter of metastatic lymph nodes was significantly different than benign nodes (3.4 mm vs. 2.4 mm; P = 0.025). Area under the curve for malignancy was higher for density-based lymph node analysis compared with size measurements (0.87 vs. 0.7).

CONCLUSION

XRA of metastatic mediastinal lymph nodes is significantly higher than benign nodes in this lung cancer model. This suggests that information on nodal density may be useful when used in combination with the results of FDG-PET in determining the likelihood of malignant adenopathy.

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

OBJECTIVES

To evaluate the clinical implications of lymph node (LN) density on ¹⁸F-FDG PET/CT for mediastinal LN characterization in non-small cell lung cancer (NSCLC).

METHODS

One hundred and fifty-two patients with 271 mediastinal LNs who underwent PET/CT and endobronchial ultrasound-guided transbronchial needle aspiration for staging were enrolled. Maximum standardized uptake value (SUVmax), short axis diameter, LN-to-primary cancer ratio of SUVmax, and median Hounsfield unit (HU) based on CT histogram were correlated to histopathology.

RESULTS

Of 271 nodes, 162 (59.8 %) were malignant. SUVmax, short axis diameter, and LPR of malignant LNs were higher than those of benign nodes. Among malignant LNs, 71.0 % had median HU between 25 and 45, while 78.9 % of benign LNs had values <25 HU or >45 HU. Using a cutoff value of 4.0, SUVmax showed the highest diagnostic ability for detecting malignant LNs with a specificity of 94.5 %, but showing a sensitivity of 70.4 %. Using additional density criteria (median HU 25-45) in LNs with 2.0< SUVmax ≤4.0, the sensitivity increased to 88.3 % with the specificity of 82.6 %.

CONCLUSIONS

LN density is useful for the characterization of LNs with mild ¹⁸F-FDG uptake. The risk of mediastinal LN metastasis in NSCLC patients could be further stratified using both ¹⁸F-FDG uptake and LN density.

KEY POINTS

• SUVmax showed the highest diagnostic ability for detecting malignant LNs. • LN density was useful in characterization of LNs with mild FDG uptake. • SUVmax and LN density together could stratify the risk of LN metastasis.

Pulmonary ground-glass nodules diagnosis: mean change rate of peak CT number as a discriminative factor of pathology during a follow-up

OBJECTIVE

We aimed to analyse the peak CT number (PEAK) in CT number histogram of ground-glass nodules (GGN), meaning the most frequent density of pixels in the image of pulmonary nodule, based on three-dimensional (3D) reconstructive model pre-operatively, and the mean rate of PEAK change (V-PEAK) during a follow-up of GGN for differential diagnosis between pre-invasive adenocarcinoma (PIA) and invasive adenocarcinoma (IAC).

METHODS

CT number histogram of pixels in GGN was made automatically by 3D measurement software. Diameter, total volume, PEAK and V-PEAK were measured from CT data sets of different groups classified by pathology, subtype and number of GGN, respectively.

RESULTS

Among all 102 cases, 47 were PIA, including atypical adenomatous hyperplasia (n = 29) and adenocarcinoma in situ (n = 18), and 55 were IAC, including minimally IAC (MIA, n = 4). By Wilcoxon test, PEAK of IAC was significantly higher than that of PIA (p < 0.001). By receiver operating curve analysis, area under the curve (AUC) was 0.857 and threshold -820.50 Hounsfield units (HU) for differentiation between PIA and IAC. V-PEAK of IAC was unexpectedly remarkably smaller than that of PIA (p < 0.001) with AUC and threshold being 0.810 and -0.829 HU day(-1), respectively.

CONCLUSION

Pre-operative PEAK and V-PEAK, which interpret and evaluate the change of volume and density of pulmonary nodule simultaneously from both exterior and interior perspectives, can help to distinguish IAC from PIA.

ADVANCES IN KNOWLEDGE

This study provided researchers of GGN another perspective, taking both volume and density of nodules into consideration for pathological evaluation.

Watch the weathercock: changes in re-staging 18F-FDG PET/CT scan predict the probability of relapse in locally advanced non-small cell lung cancer

INTRODUCTION

Induction treatment is be coming the gold standard for locally advanced non-small cell lung cancers (LA-NSCLC). In contrast to baseline positron emission/computed tomography scan (PET/CT scan), re-staging PET/CT scan has been poorly studied in LA-NSCLC.

MATERIALS AND METHODS

We retrospectively explored the efficacy of re-staging PET/CT scan to diagnose response and to predict disease-free survival (DFS) in 55 induction-treated LA-NSCLC further treated with curative surgery or radiation but not with adjuvant therapy.

RESULTS

Re-staging N status by PET/CT scan significantly correlated with pathological N status. Radiological or metabolic response in the re-staging PET/CT scan was associated with a significantly better DFS, which decreased from 25.8 to 19.3, to 11.2, and to 9.4 months in cN0, cN1, cN2, and cN3 patients, respectively.

CONCLUSION

Re-staging PET/CT scan helps to define response and consolidation treatment in induction-treated LA-NSCLC and predicts DFS. Further extended studies should confirm our results.