Radiomic Analysis using Density Threshold for FDG-PET/CT-Based N-Staging in Lung Cancer Patients. Mol Imaging Biol. 2017;19:315-322. [PMID: 27539308 DOI: 10.1007/s11307-016-0996-z]

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.

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives

The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer

PURPOSE

 The aim of this study was to investigate the potential of multiparametric 18F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients.

MATERIALS AND METHODS

 A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language.

RESULTS

 Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91 % and specificity of 92 %. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83 %, specificity of 67 % and an AUC of 0.82.

CONCLUSION

 M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data.

KEY POINTS

  · Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers. · Prediction of M-stage is superior to N-stage. · Multiparametric PET/MRI displays a valuable platform for radiomics analyses .

Predicting the Efficacy of SBRT for Lung Cancer with 18F-FDG PET/CT Radiogenomics

Purpose: to develop a radiogenomic model on the basis of 18F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment. Methods: A total of 123 patients with lung cancer who had undergone 18F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients’ PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model. Results: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849–0.851). Conclusions: The radiogenomic model based on 18F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.

[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.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [¹⁸F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([¹⁸F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [¹⁸F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [¹⁸F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [¹⁸F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

OBJECTIVE

To investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (Model_AP, Model_VP and Model_Combination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort.

RESULTS

A total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of Model_AP, Model_VP and Model_Combination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682-0.948), 0.7485 (95% CI 0.602-0.895), and 0.8772 (95% CI 0.780-0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between Model_AP and Model_Combination (P = 0.0396) and between Model_VP and Model_Combination (P = 0.0465). However, the difference in AUCs between Model_AP and Model_VP was not significant (P = 0.5061). These results demonstrate that Model_Combination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model.

CONCLUSIONS

We developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

Objectives

To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective.

Materials and Methods

In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively.

Results

Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively.

Conclusions

Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.

An Artificial Intelligence Algorithm to Predict Nodal Metastasis in Lung Cancer

BACKGROUND

Endobronchial Ultrasound (EBUS) features have high accuracy for predicting lymph node (LN) malignancy. However, their clinical application remains limited due to high operator dependency. We hypothesized that an Artificial Intelligence algorithm (NeuralSeg) is capable of accurately identifying and predicting LN malignancy based on EBUS images.

METHODS

In the derivation phase, EBUS images were segmented twice by an endosonographer and used as controls in 5-fold cross-validation training of NeuralSeg. In the validation phase, the algorithm was tested on new images it had not seen before. Logistic regression and receiver operator characteristic curves were used to determine NeuralSeg's capability of discrimination between benign and malignant LNs, using pathologic specimens as gold standard.

RESULTS

In total, 298 LNs from 140 patients were used for derivation and 108 LNs from 47 patients for validation. In the derivation cohort, NeuralSeg was able to predict malignant LNs with an accuracy of 73.8% (95% CI: 68.4% to 78.7%). In the validation cohort, NeuralSeg had an accuracy of 72.9% (95% CI: 63.5% to 81.0%), a specificity of 90.8% (95% CI: 81.9% to 96.2%) and negative predictive value (NPV) of 75.9% (95% CI: 71.5% to 79.9%). NeuralSeg showed higher diagnostic discrimination during validation compared to derivation (c-statistic= 0.75 [95% CI: 0.65-0.85] vs c-statistic=0.63 [95% CI: 0.54-0.72]).

CONCLUSIONS

NeuralSeg is able to accurately rule out nodal metastasis and can possibly be used as an adjunct to EBUS when nodal biopsy is not possible or inconclusive. Future work to evaluate the algorithm in a clinical trial will be required.

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.

Positron Emission Tomography-Based Short-Term Efficacy Evaluation and Prediction in Patients With Non-Small Cell Lung Cancer Treated With Hypo-Fractionated Radiotherapy

Background

Positron emission tomography is known to provide more accurate estimates than computed tomography when staging non–small cell lung cancer. The aims of this prospective study were to contrast the short-term efficacy of the two imaging methods while evaluating the effects of hypo-fractionated radiotherapy in non-small cell lung cancer, and to establish a short-term efficacy prediction model based on the radiomics features of positron emission tomography.

Methods

This nonrandomized-controlled trial was conducted from March 2015 to June 2019. Thirty-one lesions of 30 patients underwent the delineation of the regions of interest on positron emission tomography and computed tomography 1 month before, and 3 months after hypo-fractionated radiotherapy. Each patient was evaluated for the differences in local objective response rate between the two images. The Kaplan Meier method was used to analyze the local objective response and subsequent survival duration of the two imaging methods. The 3D Slicer was used to extract the radiomics features based on positron emission tomography. Least absolute shrinkage and selection operator regression was used to eliminate redundant features, and logistic regression analysis was used to develop the curative-effect-predicting model, which was displayed through a radiomics nomogram. Receiver operating characteristic curve and decision curve were used to evaluate the accuracy and clinical usefulness of the prediction model.

Results

Positron emission tomography-based local objective response rate was significantly higher than that based on computed tomography [70.97% (22/31) and 12.90% (4/31), respectively (p<0.001)]. The mean survival time of responders and non-responders assessed by positron emission tomography was 28.6 months vs. 11.4 months (p=0.29), whereas that assessed by computed tomography was 24.5 months vs. 26 months (p=0.66), respectively. Three radiomics features were screened to establish a personalized prediction nomogram with high area under curve (0.94, 95% CI 0.85–0.99, p<0.001). The decision curve showed a high clinical value of the radiomics nomogram.

Conclusions

We recommend positron emission tomography for evaluating the short-term efficacy of hypo-fractionated radiotherapy in non-small cell lung cancer, and that the radiomics nomogram could be an important technique for the prediction of short-term efficacy, which might enable an improved and precise treatment.

Registration number/URL

ChiCTR1900027768/http://www.chictr.org.cn/showprojen.aspx?proj=46057

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.

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.

2-[18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease

Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[¹⁸F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[¹⁸F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[¹⁸F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

Background

Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients.

Methods

This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference.

Results

In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models.

Conclusions

In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable.

[Study Progress of Radiomics in Precision Medicine for Lung Cancer]

Precision medicine, imaging first. Radiomics, a method of machine learning in artificial intelligence, provides valuable diagnostic, prognostic or predictive information through quantitative analysis on the tumor's region of interest to support personalized clinical decisions and improve individualized treatment, which could lay a solid foundation for achieving the precision medicine. This review provides a latest advance of the radiomic application of the precision medicine for lung cancer.

Systematic review and meta-analysis of the accuracy of 18F-FDG PET/CT for detection of regional lymph node metastasis in esophageal squamous cell carcinoma

Background

We performed a systematic review and meta-analysis to assess the accuracy of 18F-fluorodeoxyglucose positron emission tomography with computer tomography (18F-FDG PET/CT) for detection of regional lymph node metastasis in esophageal squamous cell carcinoma in per-patient and per-nodal station basis.

Methods

Electronic databases were researched for studies assessing the sensitivity and specificity of PET/CT to detect the regional lymph node metastasis published between January 2006 and December 2017 on esophageal squamous cell carcinoma. STATA software was performed to assess the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odd ratio (DOR) and summary receiver operating characteristic (SROC) curve. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Deeks' Funnel Plot Asymmetry Test were performed to evaluate the study quality and publication bias of included studies.

Results

Nineteen studies were eligible for meta-analysis, comprising 1,089 patients with esophageal cancer who underwent 18F-FDG PET/CT before surgery. According to the content of the article, we divided the selected studies into per-patient basis group and per-nodal basis group (one of the articles was involved in both groups). For the per-nodal station basis group (12 studies, 5,681 stations), the pooled sensitivity and specificity estimates of 18F-FDG PET/CT for detecting regional lymph node metastasis were 66% [95% confidence interval (CI): 51-78%] and 96% (95% CI: 92-98%), respectively. The corresponding values on a per-patient basis group (8 studies; 506 patients) were 65% (95% CI: 49-78%) and 81% (95% CI: 69-89%) in sensitivity and specificity, respectively.

Conclusions

Overall, 18F-FDG PET/CT have a moderate to low sensitivity and a high to moderate specificity for detection of regional nodal metastasis in esophageal cancer. Therefore, since the false rate is considerable, extending the extent of lymph node dissection or radiotherapy target volume is necessary after diagnosis of regional nodal metastasis by 18F-FDG PET/CT.

Computed Tomographic Studies of Noncalcified Nodules Related to Neuroendocrine Lung Tumor Using 68Gallium-Tagged Somatostatin Variant for Improvement in Diagnosis: A Non-Experimental, Non-Randomized, Cross-Sectional Study

Background

¹⁸Fluoro-fluorodeoxyglucose (FDG)- based positron-emission computed tomography (PET) has less specificity for noncalcified nodules (NNs). Somatostatin receptors affect the expression of normal and malignant cells. The purpose of the study was to compare the sensitivity, specificity, and accuracy of ⁶⁸Gallium-tagged DOTA-octreotate (Ga-tDO) with that of FDG PET for diagnosis of newly detected and/or untreated NNs in lung cancer patients.

Material/Methods

A total of 45 patients with lung cancer were included in the cross-sectional study and underwent Ga-tDO and FDG PET. We further confirmed observed outcomes by testing immune histochemical staining for subtype 2A of somatostatin receptor in a granuloma tissue array. The chi-square test was performed for sensitivity and specificity of predictive values among the 3 diagnostic modalities. McNemar’s test was performed to compare accuracy between Ga-tDO and FDG PET. Results were considered significant at 95% confidence level.

Results

Ga-tDO had less sensitivity (69% vs. 89%) but more specificity (91% vs. 78%) than FDG PET. Ga-tDO and FDG PET were characterized as 36 and 6 and in 36 and 3 lesions as accurate and inaccurate, respectively. There was an insignificant difference between Ga-tDO and FDG PET regarding diagnostic accuracy (p=0.7). Dosimetry results showed that the lungs were one of the least critically affected organs.

Conclusions

Ga-tDO was more specific but less sensitive than FDG PET scanning and imaging.

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.

Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings

BACKGROUND

Recent studies have shown that magnetic resonance (MR) radiomic analysis is feasible and has some value in identifying tumor characteristics, but there are few data regarding the role of MR-based radiomic features in rectal cancer.

PURPOSE

The aim of this study was to determine whether radiomic features extracted from T2 -weighted imaging (T2 WI) can identify pathological features in rectal cancer.

STUDY TYPE

Retrospective study.

POPULATION/SUBJECTS

A cohort comprising 119 rectal cancer patients who underwent surgery between January 2015 and November 2016.

FIELD STRENGTH/SEQUENCE

3.0T, axial high-resolution T2 -weighted turbo spin echo (TSE) sequence.

ASSESSMENT

Patients were classified according to pathological features such as T stage, N stage, perineural invasion, histological grade, lymph-vascular invasion, tumor deposits, and circumferential resection margin (CRM). The whole tumor volume (WTV) was distinguished, and segments were quantified on axial high-resolution T2 WI by a radiologist. A total of 256 radiomic features were extracted.

STATISTICAL TESTS

To achieve reliable results, cluster analysis and least absolute shrinkage and selection operator (LASSO) were implemented. In the cluster analysis, the patients were divided into two groups, and chi-square tests were performed to investigate the relationship between the pathological features and the radiomic-based clusters. The area under the curve (AUC) was calculated to evaluate the predictability of the model in the LASSO analysis.

RESULTS

The cluster results revealed that patients could be stratified into two groups, and the chi-square test results indicated that the pT stage was correlated with the radiomic feature cluster results (P = 0.002). The prediction model AUC for the diagnostic T stage was 0.852 (95% confidence interval: 0.677-1; sensitivity: 79.0%, specificity: 82.0%).

DATA CONCLUSION

The use of MRI-derived radiomic features to identify the T stage is feasible in rectal cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Mediastinal node staging by positron emission tomography-computed tomography and selective endoscopic ultrasound with fine needle aspiration for patients with upper gastrointestinal cancer: Results from a regional centre

AIM

To investigate the impact of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) and positron emission tomography-computed tomography (PET-CT) in the nodal staging of upper gastrointestinal (GI) cancer in a tertiary referral centre.

METHODS

We performed a retrospective review of prospectively recorded data held on all patients with a diagnosis of upper GI cancer made between January 2009 and December 2015. Only those patients who had both a PET-CT and EUS with FNA sampling of a mediastinal node distant from the primary tumour were included. Using a positive EUS-FNA result as the gold standard for lymph node involvement, the sensitivity, specificity, positive and negative predictive values (PPV and NPV) and accuracy of PET-CT in the staging of mediastinal lymph nodes were calculated. The impact on therapeutic strategy of adding EUS-FNA to PET-CT was assessed.

RESULTS

One hundred and twenty one patients were included. Sixty nine patients had a diagnosis of oesophageal adenocarcinoma (Thirty one of whom were junctional), forty eight had oesophageal squamous cell carcinoma and four had gastric adenocarcinoma. The FNA results were inadequate in eleven cases and the PET-CT findings were indeterminate in two cases, therefore thirteen patients (10.7%) were excluded from further analysis. There was concordance between PET-CT and EUS-FNA findings in seventy one of the remaining one hundred and eight patients (65.7%). The sensitivity, specificity, PPV and NPV values of PET-CT were 92.5%, 50%, 52.1% and 91.9% respectively. There was discordance between PET-CT and EUS-FNA findings in thirty seven out of one hundred and eight patients (34.3%). MDT discussion led to a radical treatment pathway in twenty seven of these cases, after the final tumour stage was altered as a direct consequence of the EUS-FNA findings. Of these patients, fourteen (51.9%) experienced clinical remission of a median of nine months (range three to forty two months).

CONCLUSION

EUS-FNA leads to altered staging of upper GI cancer, resulting in more patients receiving radical treatment that would have been the case using PET-CT staging alone.