18F-FDG PET/CT diagnostic performance in solitary and multiple pulmonary nodules detected in patients with previous cancer history: reports of 182 nodules

18F-FDG distribution pattern improves the diagnostic accuracy of single pulmonary solid nodule

Background

The main purpose is to explore the use of visual assessment of the heterogeneous distribution of ¹⁸F-FDG in single pulmonary solid lesions to differentiate the benign from the malignant.

Methods

The 200 cases of pulmonary nodules or masses examined by ¹⁸F-FDG PET/CT were retrospectively analyzed. The heterogeneity of ¹⁸F-FDG distribution of the lesion was visually and quantitatively evaluated and the higher part of metabolism was observed and measured at the proximal or distal part to determine the lesion nature. The sensitivity, specificity, PPV, NPV, and accuracy of this method were calculated.

Results

Total 171 pulmonary lesions showed heterogeneity of ¹⁸F-FDG uptake, including the 111 malignant and 60 benign. 54/60 (90.00%) benign lesions showed higher ¹⁸F-FDG uptake visually at distal part, while 104/111 (93.69%) malignant lesions showed higher ¹⁸F-FDG uptake visually at the proximal part. This visual method has good repeatability with a high kappa value (0.821, p<0.001). 52/60 (86.67%) benign lesions showed higher ¹⁸F-FDG uptake quantitatively at distal part, while 107/111 (96.40%) malignant lesions showed higher ¹⁸F-FDG uptake quantitatively at the proximal part. The sensitivity, specificity, PPV, NPV and accuracy of visual and quantitative methods were 93.69%; 96.40%, 90.0%; 86.67%, 94.55%; 93.04%, 88.52%; 92.86%, 92.40%; 92.98%, respectively (p<0.001). When combining the metabolic value and morphological characteristics of PET/CT with visual¹⁸F-FDG heterogeneous features, the accuracy reached to 98.25%. The other 29 lesions (14.5%) with no heterogeneity were smaller (2.17 ± 1.06 vs 3.58 ± 1.48, P<0.001).

Conclusions

Benign and malignant lung lesions showed different heterogeneity of ¹⁸F-FDG uptake. Lung cancer can be effectively distinguished from infectious or inflammatory lesions by this simple and convenient method.

Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans

Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. We tested a total of three conventional imaging features, six form factors, and two shape features for significant differences between benign and malignant lung lesions on CT scans. The study population consisted of 192 lung nodules from two independent datasets, containing 109 (38 benign, 71 malignant) and 83 (42 benign, 41 malignant) lung lesions, respectively. The standard of reference was either histological evaluation or stability on radiological followup. The statistical significance was determined via the Mann-Whitney U nonparametric test, and the ability of the form factors to discriminate a benign vs. a malignant lesion was assessed through multivariate prediction models based on Support Vector Machines. The univariate analysis returned four form factors (Angelidakis compactness and flatness, Kong flatness, and maximum projection sphericity) that were significantly different between the benign and malignant group in both datasets. In particular, we found that the benign lesions were on average flatter than the malignant ones; conversely, the malignant ones were on average more compact (isotropic) than the benign ones. The multivariate prediction models showed that adding form factors to conventional imaging features improved the prediction accuracy by up to 14.5 pp. We conclude that form factors evaluated on lung nodules on CT scans can improve the differential diagnosis between benign and malignant lesions.

Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules

Objectives

This study aimed to evaluate the ability of ¹⁸fluorine-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN).

Methods

Data of 48 patients with SPN detected on ¹⁸F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC).

Results

The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%).

Conclusion

Machine learning based on ¹⁸F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.

Ultrafast 30-s total-body PET/CT scan: a preliminary study

PURPOSE

The aim of this study is to explore the diagnostic value of the images obtained in ultrafast 30-s acquisition time by the total-body PET/CT (¹⁸F-FDG injection dose of about 3.7 MBq/kg), and to evaluate whether they can meet the requirements of clinical diagnosis or not.

METHODS

This retrospective study explored the clinical value of ultrafast 30-s ¹⁸F-FDG total-body PET/CT in 88 oncology patients, using the post-surgical pathological diagnosis as the reference standard. The data were acquired over 300 s and reconstructed using all 300-s data (G300) and only the initial 30 s (G30). Two readers independently assessed all images qualitatively and quantitatively. The diagnostic performance was compared between G300 and G30.

RESULTS

The G300 average qualitative score was higher than G30 (P < 0.001). G300 and G30 also differed quantitatively in the liver and mediastinum SUV_max, SD, and SNR (all P < 0.001), but had similar sensitivities (89.09% vs. 86.36%, P = 0.250). The G300 group had higher accuracy (79.73%) and a larger area under the curve (0.709) than G30 (77.70% and 0.695, respectively; all P > 0.05).

CONCLUSION

The 30-s total-body PET/CT could meet clinical diagnostic requirements for malignant tumors in patients intolerant to prolonged horizontal positioning.

Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography

Background

The investigation of incidental pulmonary nodules has rapidly become one of the main indications for 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). There is also a growing trend to use artificial Intelligence for optimization and interpretation of PET-CT Images. Therefore, we proposed a novel deep learning model that aided in the automatic differentiation between malignant and benign pulmonary nodules on FDG PET-CT.

Methods

In total, 112 participants with pulmonary nodules who underwent FDG PET-CT before surgery were enrolled retrospectively. We designed a novel deep learning three-dimensional (3D) high-resolution representation learning (HRRL) model for the automated classification of pulmonary nodules based on FDG PET-CT images without manual annotation by experts. For the images to be localized more precisely, we defined the territories of the lungs through a novel artificial intelligence-driven image-processing algorithm, instead of the conventional segmentation method, without the aid of an expert; this algorithm is based on deep HRRL, which is used to perform high-resolution classification. In addition, the 2D model was converted to a 3D model.

Results

All pulmonary lesions were confirmed through pathological studies (79 malignant and 33 benign). We evaluated its diagnostic performance in the differentiation of malignant and benign nodules. The area under the receiver operating characteristic curve (AUC) of the deep learning model was used to indicate classification performance in an evaluation using fivefold cross-validation. The nodule-based prediction performance of the model had an AUC, sensitivity, specificity, and accuracy of 78.1, 89.9, 54.5, and 79.4%, respectively.

Conclusion

Our results suggest that a deep learning algorithm using HRRL without manual annotation from experts might aid in the classification of pulmonary nodules discovered through clinical FDG PET-CT images.

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Purpose

This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (¹⁸F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs).

Methods

We retrospectively analyzed patients with suspicious GGNs who underwent ¹⁸F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks).

Results

A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73].

Conclusion

The 3D-CNN based on ¹⁸F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00423-1.

[Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]

局部热消融技术在肺部结节治疗领域正处在起步与发展阶段，为了肺结节热消融治疗的临床实践和规范发展，由“中国医师协会肿瘤消融治疗技术专家组”“中国医师协会介入医师分会肿瘤消融专业委员会”“中国抗癌协会肿瘤消融治疗专业委员会”“中国临床肿瘤学会消融专家委员会”组织多学科国内有关专家，讨论制定了“热消融治疗肺部亚实性结节专家共识（2021年版）”。主要内容包括：①肺部亚实性结节的临床评估；②热消融治疗肺部亚实性结节技术操作规程、适应证、禁忌证、疗效评价和相关并发症；③存在的问题和未来发展方向。

Characterization of 18F-fluorodeoxyglucose metabolic spatial distribution improves the differential diagnosis of indeterminate pulmonary nodules and masses with high fluorodeoxyglucose uptake

Background

The aim of this study was to investigate the value of visual assessment of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) metabolic spatial distribution (V-FMSD) in the diagnosis of indeterminate pulmonary nodules and masses with high ¹⁸F-FDG uptake.

Methods

A total of 301 patients with indeterminate pulmonary nodules or masses who underwent ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) imaging were retrospectively studied. The characteristics of ¹⁸F-FDG metabolic spatial distribution (FMSD) in the proximal and distal regions of the lesions were visually analyzed using a 5-point scoring system. The sensitivity, specificity, accuracy, and area under receiver operating characteristic curve (AUC) were compared between V-FMSD and conventional PET/CT methods for the diagnosis of hypermetabolic indeterminate pulmonary nodules and masses.

Results

The V-FMSD results showed that 180 (92.8%) malignant lesions had a score of ≥3 and 78 (72.9%) benign lesions had a score of ≤2. This indicated that the FMSD in the proximal region of malignant lesions was significantly higher than that of the distal region, and the FMSD in the proximal region of benign lesions was significantly lower than that of the distal region. V-FMSD had a specificity of 72.9%, which was markedly higher than those of the maximum standard uptake value (SUV_max; 0%, P<0.001) and the retention index (RI; 26.2%, P<0.001). The AUC of V-FMSD was 0.886, which was significantly larger than those of the SUV_max (0.626, P<0.001), RI (0.670, P<0.001), and PET/CT (0.788, P<0.05).

Conclusions

Our study found that pulmonary benign and malignant lesions have distinct FMSD characteristics. V-FMSD can therefore be used as a novel auxiliary marker to improve the diagnostic accuracy of hypermetabolic indeterminate pulmonary nodules and masses.

Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation

In this paper, we investigate the role of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign (n=39) or malignant (n=72) solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4-11.2 pp and 2.2-10.2 pp, respectively. In conclusion, we found that shape and texture features from 18F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.

Mycobacterium szulgai Lung Disease or Breast Cancer Relapse-Case Report

Cancers are one of the risk factors of non-tuberculous mycobacterial (NTM) lung disease. The majority of data in this group of patients concern infections caused by Mycobacterium avium—the most prevalent NTM species worldwide. In contrast, limited information can be found regarding the uncommon NTM such as Mycobacterium szulgai. We present the case of M. szulgai lung disease in a patient with a history of breast cancer. Coexistence of NTM lung disease and breast cancer lung metastasis as well as primary lung cancer was suspected. Finally, neoplastic disease was ruled out based on negative results of endobronchial biopsy and negative tumor markers for lung and breast cancer. M. szulgai lung disease was successfully treated with rifampicin, ethambutol and clarithromycin.

Radioguided Surgery, a Cost-Effective Strategy for Treating Solitary Pulmonary Nodules: 20-Year Experience of a Single Center

INTRODUCTION

Surgical resection with minimally invasive approach is the gold standard for both definitive diagnosis and treatment of solitary pulmonary nodules (SPNs); however, it can be difficult to pinpoint small, deep, or subsolid nodes without palpating lung parenchyma. The primary endpoint of this study is showing that radioguided surgery is a cost-effective strategy to improve the effectiveness of video-thoracoscopic localization/resection of SPNs/ground-glass opacities (GGOs). Secondary endpoints are analyzing the morbidity of this technique and tips and tricks to better manage this method.

METHODS

SPN smaller than 20 mm and/or with a distance from the visceral pleura ≥5 mm underwent minimally invasive resection after computed tomography-guided injection of a solution composed of 0.1/0.2 mL of 99Tc-labeled human serum albumin microspheres and 0.1 mL of nonionic contrast. In the operating theater, a collimated probe connected to a gamma ray detector allowed localization of the target area.

RESULTS

Between 1997 and 2018, a total of 451 patients with SPN/GGO underwent minimally invasive surgery with a radioguided technique at our hospital. The mean SPN diameter was 13 mm (range, 5-20 mm), and the mean distance from the visceral pleura was 15 mm (range, 6-29 mm). The mean time to a localizing nodule was 3 minutes (range, 1-5 minutes). No significant injection-related complications were reported; only 3.3% of patients (15 of 451) developed pneumothorax. Both 30- to 60-day and 90-day mortality were 0%. The rate of postoperative complications was 2.53% (prolonged air leak). The conversion rate to thoracotomy was 1.55% (7 of 451).

CONCLUSIONS

Our 20-year experience shows that radioguided thoracoscopic surgery is a safe and feasible strategy to treat suspicious SPN/GGO, with a success rate of 98%.

PET/CT Radiomics in Lung Cancer: An Overview

Quantitative extraction of imaging features from medical scans (‘radiomics’) has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack of standardization and repeatability. Such limitations, along with the unmet demand for large enough image datasets for training the algorithms, are major hurdles that still limit the application of radiomics on a large scale. In this paper, we review the current developments, potential applications, limitations, and perspectives of PET/CT radiomics with specific focus on the management of patients with lung cancer.

The diagnostic performance of PET/CT scans for the detection of para-aortic metastatic lymph nodes in patients with cervical cancer: A meta-analysis

Objective

We performed a meta-analysis to evaluate the diagnostic value of positron emission tomography/computed tomography (PET/CT) in the detection of para-aortic lymph node metastasis in cervical cancer.

Methods

We searched the PubMed, Embase, Web of Science, Cochrane Library, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang and VIP databases in all languages from their inception to September 2018. Stat15.0 software was used to obtain pooled estimates of sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) as well as a summary receiver operating characteristic (SROC) curves. Deek‘s funnel plot was used to assess publication bias. QUADAS-2 was used to evaluate the quality of the studies. The protocol for this meta-analysis is registered in PROSPERO (CRD42019115330).

Results

We obtained 14 studies, and the pooled estimates for sensitivity and specificity of PET/CT were 0.71 (95% confidence interval (CI) = 0.54–0.83) and 0.97 (95% CI = 0.93–0.98), respectively. Pooled PLR and NLR were 21.53 and 0.30, respectively. The diagnostic odds ratio (DOR) was70.59, and the area under the curve (AUC) was 0.95.

Conclusion

PET/CT is an effective and important imaging method for the diagnosis of para-aortic lymph node metastasis in early cervical cancer.