Retrospective analysis for the false positive diagnosis of PET-CT scan in lung cancer patients

False Positive Rate from Prospective Studies of PET-CT in Cutaneous Malignant Melanoma: A Systematic Review and Meta-Analysis

BACKGROUND

Cutaneous malignant melanoma (CMM) is increasing in prevalence and possesses the highest mortality rate of any skin cancer. Positron Emission Tomography and Computed Tomography (PET-CT) may be utilised in either radiological staging or surveillance, primarily in stage III-IV disease. False positive (FP) results lead to patient distress, increased costs, and unnecessary follow-up. The FP rate in CMM literature varies widely, altering calculations of positive predictive value and has not undergone pooled meta-analytic.

MATERIALS AND METHODS

A systematic review and meta-analysis of FP results in prospective studies of PET-CT in CMM was performed in accordance with PRISMA guidelines.

RESULTS

The systematic review produced 14 trials for inclusion. Patient-based reporting had the lowest pooled proportion of FP results with 5.8 % (95 % CI = 3.3 % to 8.8 %), lesion-based was highest with 9.1 % (95 % CI = 3.4 % to 17.2 %) and combined was 6.1 % (95 % CI = 4.3 % to 8.1 %). Bias was low to unclear other than for FP reporting. Heterogeneity (I2) was variable across all analyses. FP findings were mainly lymphatic, dermatological, respiratory, or skeletal. Diagnostic information was not provided.

CONCLUSIONS

This study was the first attempt to quantify the pooled proportion of FP results from PET-CT in CMM. A small number of studies (n = 14) were available due to the predominance of retrospective methodology. Due to inconsistent reporting the true proportion of FP results is unclear. Systemic distribution was expected but limited diagnostic information was provided. Repeat meta-analysis using retrospective work should be performed. Future work should be prospective with clearly documented FP proportion, distribution, diagnosis, and follow-up.

Combination of circulating tumor cells and 18F-FDG PET/CT for precision diagnosis in patients with non-small cell lung cancer

PURPOSE

To investigate the value of 2-deoxy-18f-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) and circulating tumor cells (CTCs) for the differential diagnosis of patients with benign lung diseases and those with NSCLC. To explore the phenotypic heterogeneity of CTCs and their correlation with FDG uptake in patients with Stage I-IV NSCLC.

METHODS

Blood specimens from patients with benign lung diseases and patients with primary NSCLC were collected for the detection of CTCs and their subtypes (epithelial, mixed, and mesenchymal) and analyzed for 18F-FDG PET/CT tumor metabolic parameters, including the maximum standardized uptake value (SUVmax), standard uptake value (SUL), metabolic tumor volume of primary lesion (MTV), total lesion glycolysis of primary lesion (TLG). Clinical data including age, gender, smoking history, tumor size, TNM stage and pathology type were also collected. The value of the two method alone and in combination for the differential diagnosis of benign and malignant was comparatively analyzed. Finally, the differences in CTC and its subtypes in different stages of NSCLC were compared, and FDG metabolic parameters were correlated with CTC subtypes.

RESULTS

There were a total of 65 patients with pulmonary diseases, including 12 patients with benign pulmonary diseases and 53 patients with NSCLC. The mean age was 67 ± 10 (38-89 years), 27 were females and 38 were males. 31 (22 males and 9 females) had a long history of smoking. The mean size of the largest diameter of all single lesions was 36 ± 22 mm with a range of 10-108 mm. Seven out of 12 benign diseases were inflammatory granulomatous lesions and 5 were inflammatory pseudotumours. Twenty-four out of 53 NSCLC were adenocarcinomas and 29 were squamous carcinomas. Twelve out of 53 patients with NSCLC were in Stage I, 10 were in Stage II, 17 were in Stage III and 14 were in Stage IV. SUVmax, SUL, MTV, TLG, total CTCs, epithelial CTCs, and mixed CTCs were all valuable in the differential diagnosis of benign and malignant. TLG combined with mixed CTCs was statistically different from all other diagnostic methods (p < 0.05) and higher than any other diagnostic criteria. In the differential diagnosis of benign and Stage I NSCLC, only total CTC (Z = -2.188 p = 0.039) and mixed CTCs (Z = -3.020 p = 0.014) had certain diagnostic efficacy, and there was no statistical difference between them (p = 0.480). Only mesenchymal CTCs differed in Stage I-IV NSCLC, with a higher number of those who developed distant metastases than those who had non-distant metastases. Epithelial CTCs correlated with SUVmax (r = 0.333, p = 0.015) and SUL (r = 0.374, p = 0.006). Mmesenchymal CTCs correlated with MTV (r = 0.342, p = 0.018) and TLG (r = 0.319, p = 0.02). Further subgroup analyses revealed epithelial CTCs were correlated with SUVmax (r = 0.543, p = 0.009) and SUL (r = 0.552, p = 0.008), and the total CTCs was correlated with SUVmax (r = 0.622, p = 0.003), SUL (r = 0.652, p = 0.003), MTV (r = 0.460, p = 0.031), and TLG (r = 0.472, p = 0.027) in the early group (Stage I-II). Only mesenchymal CTCs was associated with MTV (r = 0.369, p = 0.041), and TLG (r = 0.415, p = 0.02) in the intermediate-late group (Stage III-IV).

CONCLUSION

Both FDG PET metabolic parameters and CTCs demonstrated diagnostic value for NSCLC, and combining TLG with mixed CTCs could enhance their diagnostic efficacy. The total CTCs and mixed CTCs showed greater diagnostic value than FDG PET in distinguishing benign lesions from Stage I NSCLC. In NSCLC patients, the epithelial CTCs exhibited a positive correlation with SUVmax and SUL, while mesenchymal CTCs correlated with MTV, and TLG. Besides, epithelial CTCs showed stronger correlations with SUVmax and SUL, and total CTCs showed stronger correlations with SUVmax, SUL, MTV, and TLG in Stage I-II NSCLC. Only mesenchymal CTCs in Stage III-IV NSCLC showed correlations with MTV and TLG. Stage IV NSCLC cases displayed a higher number of mesenchymal CTCs.

Diagnostic Performance of 18 F-FDG Positron Emission Tomography/Computed Tomography and Blood Test Parameters for Pulmonary Inflammatory Pseudotumor

PURPOSE

Pulmonary inflammatory pseudotumor (PIP) is an inflammatory proliferative tumor-like lesion that frequently exhibits hypermetabolism on 18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography imaging (PET/CT) and is readily misdiagnosed as a malignant tumor. The purpose of this study was to identify PIP by combining PET/computed tomography metabolic and blood test characteristics with machine learning.

PATIENTS AND METHODS

We recruited 27 patients with PIP and 28 patients with lung cancer (LC). The PET metabolic and blood test parameters were collected, and the differences between the groups were evaluated. In addition, we combined the support vector machine (SVM) classifier with the indicators that differed between the groups to classify PIP and LC.

RESULTS

For PET metabolic parameters, our findings showed that, as compared with the LC group, maximal standardized uptake value ( P < 0.001, t = -4.780), Mean standardized uptake value SUV mean , P < 0.001, t = -4.946), and SD40% ( P < 0.001, t = -4.893) were considerably reduced in the PIP group, whereas CV40% ( P = 0.004, t = 3.012) was significantly greater. For blood test parameters, the total white blood cell count ( P < 0.001, t = 6.457) and absolute neutrophil count ( P < 0.001, t = 6.992) were substantially higher in the PIP group than in the LC group. Furthermore, the performance of SVM trained solely on PET metabolic parameters (mean area under the curve [AUC] = 0.84) was comparable to that of SVM trained solely on blood test parameters (mean AUC = 0.86). Surprisingly, utilizing the combined parameters increased SVM performance significantly (mean AUC = 0.98).

CONCLUSION

PET metabolic and blood test parameters differed significantly between the PIP and LC groups, and the SVM paradigm using these significantly different features has the potential to be used to classify PIP and LC, which has important clinical implications.

A Review of the Current Approach and Treatment Landscape for Stage III Non-Small Cell Lung Cancer

The therapeutic landscape of the management of stage III non-small cell lung cancer (NSCLC) has drastically evolved with the incorporation of immunotherapy and targeted therapy. Stage III NSCLC accounts for one-third of the cases and the treatment strategy of these locally advanced presentations are diverse, ranging from surgical to non-surgical options; with the incorporation of chemo-immunotherapy, radiation, and targeted therapies wherever applicable. The staging of this disease has also changed, and it is essential to have a strong multidisciplinary approach to do justice to patient care. In this article, we aim to navigate the nuanced approaches in the diagnosis and treatment of stage III NSCLC and expand on the evolution of the management of this disease.

18F-FDG PET-CT Scans in Oncology Patients Treated with Hyaluronic Acid Filler: Not Always a Pitfall

The use of hyaluronic acid (HA) fillers in oncology patients undergoing PET-CT scans is a topic of debate due to potential interference with imaging accuracy. A 54-year-old female, postmelanoma metastasectomy in the parotid region with subsequent facial nerve palsy (FNP), received HA filler injections for facial symmetry and functional restoration. Follow-up PET-CT scans showed no interference or artifacts attributable to HA injection, allowing for accurate imaging results. This case suggests that HA fillers administered in oncology patients may not universally pose challenges or disrupt PET-CT imaging interpretation. Due to the possible false positives induced by fillers, the inclusion of aesthetic treatments in patients' anamnesis is a crucial step to accurately interpret PET-CT images. Although maintaining high level of caution in interpreting PET-CT results after filler injection is essential, our case emphasizes the safety of this procedure in oncology patients undergoing follow-up PET-CT scans.

Development of an Engineered Single-Domain Antibody for Targeting MET in Non-Small Cell Lung Cancer

The Mesenchymal Epithelial Transition (MET) receptor tyrosine kinase is upregulated or mutated in 5% of non-small-cell lung cancer (NSCLC) patients and overexpressed in multiple other cancers. We sought to develop a novel single-domain camelid antibody with high affinity for MET that could be used to deliver conjugated payloads to MET expressing cancers. From a naïve camelid variable-heavy-heavy (VHH) domain phage display library, we identified a VHH clone termed 1E7 that displayed high affinity for human MET and was cross-reactive with MET across multiple species. When expressed as a bivalent human Fc fusion protein, 1E7-Fc was found to selectively bind to EBC-1 (MET amplified) and UW-Lung 21 (MET exon 14 mutated) cell lines by flow cytometry and immunofluorescence imaging. Next, we investigated the ability of [89Zr]Zr-1E7-Fc to detect MET expression in vivo by PET/CT imaging. [89Zr]Zr-1E7-Fc demonstrated rapid localization and high tumor uptake in both xenografts with a %ID/g of 6.4 and 5.8 for EBC-1 and UW-Lung 21 at 24 h, respectively. At the 24 h time point, clearance from secondary and nontarget tissues was also observed. Altogether, our data suggest that 1E7-Fc represents a platform technology that can be employed to potentially both image and treat MET-altered NSCLC.

Head-to-Head Comparison of FDG and Radiolabeled FAPI PET: A Systematic Review of the Literature

FAPI-based radiopharmaceuticals are a novel class of tracers, mainly used for PET imaging, which have demonstrated several advantages over [18F]FDG, especially in the case of low-grade or well-differentiated tumors. We conducted this systematic review to evaluate all the studies where a head-to-head comparison had been performed to explore the potential utility of FAPI tracers in clinical practice. FAPI-based radiopharmaceuticals have shown promising results globally, in particular in detecting peritoneal carcinomatosis, but studies with wider populations are needed to better understand all the advantages of these new radiopharmaceuticals.

Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer

BACKGROUND

Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information.

METHODS

Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.

RESULTS

The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.

CONCLUSION

The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.

Machine learning approach using 18 F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection

OBJECTIVE

In this study, we aimed to evaluate the role of 18F-fluorodeoxyglucose PET/computerized tomography ( 18 F-FDG PET/CT)-based radiomic features in the differentiation of infection and malignancy in consolidating pulmonary lesions and to develop a prediction model based on radiomic features.

MATERIAL AND METHODS

The images of 106 patients who underwent 18 F-FDG PET/CT of consolidated lesions observed in the lung between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest of the lung lesions was determined and volumetric and textural features were obtained. Clinical and radiomic data were evaluated with machine learning algorithms to build a model.

RESULTS

There was a significant difference in all standardized uptake value (SUV) parameters and 26 texture features between the infection and cancer groups. The features with a correlation coefficient of less than 0.7 among the significant features were determined as SUV mean , GLZLM_SZE, GLZLM_LZE, GLZLM_SZLGE and GLZLM_ZLNU. These five features were analyzed in the Waikato Environment for Knowledge Analysis program to create a model that could distinguish infection and cancer groups, and the model performance was found to be the highest with logistic regression (area under curve, 0.813; accuracy, 75.7%). The sensitivity and specificity values of the model in distinguishing cancer patients were calculated as 80.6 and 70.6%, respectively.

CONCLUSIONS

In our study, we created prediction models based on radiomic analysis of 18 F-FDG PET/CT images. Texture analysis with machine learning algorithms is a noninvasive method that can be useful in the differentiation of infection and malignancy in consolidating lung lesions in the clinical setting.

False Positive Positron Emission Tomography/Computed Tomography (PET/CT) Requiring Biopsy for Proper Staging of Lung Cancer

Lung cancer is the leading cause of cancer death in women in developed countries. Staging is crucial in determining the treatment modality. Different treatment modalities for lung cancer include surgery, radiation therapy, and chemotherapy. PET/CT is the most sensitive and accurate modality for detecting hilar, mediastinal, and metastatic disease except in the brain. PET/CT scan often upstages the disease. PET/CT has also been shown to have false positive results. We present the case of a 72-year-old female who had a false positive finding on PET/CT, which would have changed the management process and outcome of her disease.

Pulmonary Granulomas and Mycobacterial Infection: Concordance between the Results of Special Stains Performed on Lung Tissue Sections and Tissue Cultures

Background: The most common cause of infectious pulmonary granulomas worldwide is Mycobacterium tuberculosis. The diagnosis is based on clinical presentation, histopathologic findings, detection of acid-fast bacilli (AFB) in tissue or sputum using special stains, and/or isolation of mycobacteria in cultures or via PCR-based methods. Different studies have shown that high levels of discrepancy exist between these diagnostic approaches in lung tissue specimens. Objective: To assess the degree of concordance between the results of special stains and cultures on lung tissue specimens in the diagnosis of mycobacterial infections. Methodology: Eighty-seven patients with a diagnosis of granulomas (necrotizing and non-necrotizing) on lung tissue specimens were identified. Cohen’s kappa was used to measure the general concordance between the results of the histopathological examination (special stains) and bacteriological tissue cultures. Results: With Kinyoun acid-fast stains, 8/48 (16.7%) cases were positive for AFB. With FITE stains, 10/57 (17.5%) cases were positive for AFB. There was strong agreement between Kinyoun acid-fast and FITE stains (Kappa = 0.806; p-value < 0.001). Tissue cultures were performed on 38/87 cases (43.7%), and 10/38 (26.3%) of the cultures were positive for mycobacteria. There was no concordance between Kinyoun acid-fast stains or FITE stains and tissue cultures results. Conclusion: Our observations represent an initial step in the process of reviewing the two methods used at our institution to diagnose mycobacterial infections on lung tissue specimens and highlight the need of incorporating more advanced diagnostic methods such as PCR to confirm mycobacterial infections and improve patient management. Importantly, species-level identification of mycobacteria is necessary to guide treatment.

Mediastinal lymphnode positivity clinical scoring system for lung adenocarsinoma-mediastinal lymph node evaluation and staging

Aim: The study-cohort aims to assess PET-CT's correlation with adenocarcinomas' subtypes and propose a scoring system for mediastinal lymph nodes staging. Material and Method: The patient cohort is a multicenter, retrospective analysis of 268 patient that underwent surgery for NSCLC adenocarcinoma. Preoperative PET-CT results for mediastinal lymph node staging was pathologically confirmed on tissue specimens obtained at anatomical resection. Statistical evaluation of PET CT, radiological and pathological outcomes were performed on all subgroups. Results: The low FDG affinity in the lepidic pattern was statistically significant in the study (p

Comparison of recurrence risk between patients with clinically node-positive and -negative stage I non-small cell lung cancer following surgery: A propensity score matching analysis

Background

Identifying patients with stage I non‐small cell lung cancer (NSCLC) at increased risk of tumor recurrence following surgery remains a major challenge. The current study aimed to compare disease‐free survival (DFS) rates after surgery between patients with clinically node‐positive (cN+) and ‐negative (cN0) stage I NSCLC.

Methods

Patients with pathological stage I resected NSCLC were identified from the lung cancer database of Changhua Christian Hospital in Taiwan. Patients with clinical N status 1 or 2 and pathological N status 0 were identified as the cN+/pN0 cohort, whereas others were identified as the cN0/pN0 cohort. Propensity score matching (PSM) was used to balance the baseline characteristics between both cohorts. Kaplan–Meier method and Cox proportional hazards model were used to evaluate DFS.

Results

From January 2010 to July 2019, 754 eligible patients were enrolled into the study, among whom 41 (5.4%) were cN+/pN0. The median follow‐up time was 43.4 months. Before PSM, the 5‐year DFS rate was 79.0% and 90.3% in cN+/pN0 and cN0/pN0 cohorts (log‐rank test, p = 0.009), respectively. After a 1:4 PSM, multivariate analysis showed that the cN+/pN0 cohort still had a poorer DFS compared to the cN0/pN0 cohort in (hazard ratio, 3.17; p = 0.040).

Conclusion

Among patients with stage I resected NSCLC, cN+ patients had a worse DFS compared to cN0 patients. Surgeons should therefore consider more aggressive adjuvant therapy or frequent follow‐up in patients with surgically resected stage I NSCLC with cN+ status.

PET positive tattoo lymphadenopathy: a case report and review of the literature

We present a case of positron emission tomography (PET)–avid internal mammary lymphadenopathy, prompting open excisional biopsy of the lymph node in the setting of concern about breast-implant associated anaplastic large cell lymphoma (BIA-ALCL) or breast cancer recurrence. Histopathological examination revealed reactive lymphadenopathy with tattoo ink and no malignancy. We undertook a review of the literature to investigate the frequency of tattoo ink related PET-avid lymphadenopathy, and false positive PET scan results from tattooing. Lymphadenopathy related to tattoo ink has been reported previously, however this is the only case of benign PET positive tattoo lymphadenopathy resulting in unnecessary invasive surgery reported to date.

A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Background

Clinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Methods

A total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram.

Results

The predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768–0.796) in the primary cohort and 0.813 (95% CI, 0.787–0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful.

Conclusion

This study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases

Purpose

To propose and evaluate habitat imaging-based ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics for preoperatively discriminating non-small cell lung cancer (NSCLC) and benign inflammatory diseases (BIDs).

Methods

Three hundred seventeen ¹⁸F-FDG PET/CT scans were acquired from patients who underwent aspiration biopsy or surgical resection. All volumes of interest (VOIs) were semiautomatically segmented. Each VOI was separated into variant subregions, namely, habitat imaging, based on our adapted clustering-based habitat generation method. Radiomics features were extracted from these subregions. Three feature selection methods and six classifiers were applied to construct the habitat imaging-based radiomics models for fivefold cross-validation. The radiomics models whose features extracted by conventional habitat-based methods and nonhabitat method were also constructed. For comparison, the performances were evaluated in the validation set in terms of the area under the receiver operating characteristic curve (AUC). Pairwise t-test was applied to test the significant improvement between the adapted habitat-based method and the conventional methods.

Results

A total of 1,858 radiomics features were extracted. After feature selection, habitat imaging-based ¹⁸F-FDG PET/CT radiomics models were constructed. The AUC of the adapted clustering-based habitat radiomics was 0.7270 ± 0.0147, which showed significantly improved discrimination performance compared to the conventional methods (p <.001). Furthermore, the combination of features extracted by our adaptive habitat imaging-based method and non-habitat method showed the best performance than the other combinations.

Conclusion

Habitat imaging-based ¹⁸F-FDG PET/CT radiomics shows potential as a biomarker for discriminating NSCLC and BIDs, which indicates that the microenvironmental variations in NSCLC and BID can be captured by PET/CT.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

To biopsy or not to biopsy? Outcomes following stereotactic body radiotherapy (SBRT) for biopsy-confirmed versus radiologically-diagnosed primary lung cancer in a single Australian institution

INTRODUCTION

Obtaining tissue diagnosis for lung cancer can sometimes be difficult and unsafe. We evaluated outcomes of biopsy-confirmed versus radiologically-diagnosed lung cancer treated with stereotactic body radiotherapy (SBRT).

METHODS

A single-institutional retrospective cohort of lung cancer patients treated with SBRT between February 2014 and October 2018. Outcomes of interest were: local failure (LF), distant failure (DF), and overall survival (OS). Probability of LF, DF, and OS were estimated using the Kaplan-Meier method. Differences in outcomes between biopsy-confirmed versus radiologically-diagnosed lung cancer were evaluated using the log-rank test.

RESULTS

Sixty-five lung lesions in 61 patients were treated with SBRT. Mean age was 75.6 years. Twenty-seven patients (44.3%) were ECOG 2-3. Thirty-nine patients (64%) were radiologically-diagnosed. There were five cases of LF observed at median of 12.8 months post-SBRT and 12-month LF-free survival was 96% (95% CI, 86-99%), with no differences between groups (p = 0.1). Sixteen patients developed DF, with 12-month DF-free survival of 84% (95% CI, 71-91%), and no difference between groups (p = 0.06). Sixteen deaths were reported at a median of 12.5 months post-SBRT, with 12-month OS of 85% (95% CI, 73-92%), and no differences between study groups (p = 0.5). No grade 3 toxicities were reported.

CONCLUSION

The oncological outcomes were similar in patients with early lung cancer treated with SBRT with or without biopsy-confirmation. In situations where tissue diagnosis is not feasible or unsafe, it is not unreasonable to offer SBRT based on clinical and radiological suspicion following multidisciplinary discussions.

Clinical Characteristics and Outcome of Pathologic N0 Non-small Cell Lung Cancer Patients With False Positive Mediastinal Lymph Node Metastasis on FDG PET-CT

BACKGROUND/AIM

Preoperative fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET-CT) is a non-invasive and useful diagnostic tool to evaluate mediastinal lymph node (LN) metastasis in lung cancer. However, there are often false-positive LN cases in FDG PET-CT. This study aimed to explore the clinical characteristics and outcome of pathologic N0 non-small cell lung cancer patients with false-positive mediastinal LN on FDG PET-CT.

PATIENTS AND METHODS

We enrolled 147 patients who underwent preoperative FDG PET-CT scan and mediastinal LN dissection. These patients were re-evaluated for post-operative pathologic nodal metastasis and divided into a false-positive group and a group of others.

RESULTS

Among 40 patients diagnosed with clinical N1-3 on FDG PET-CT, 19 (47.5%) patients were pathologic N0, meaning false-positive LN by PET-CT. Preoperative absolute platelet count and platelet-lymphocyte ratio were significantly higher in patients with pathologic N0. The presence of lymphatic invasion was significantly lower in patients with pathologic N0 than in the group of others. Recurrence-free survival was significantly shorter in patients with false positive LN than in patients with true positive LN or true negative LN at the same pathologic stage.

CONCLUSION

Higher absolute platelet count and PLR, lower proportion of lymphatic invasion and shorter recurrence-free survival were associated with false positive mediastinal LN on preoperative FDG PET-CT.

Discriminating Small-Sized (2 cm or Less), Noncalcified, Solitary Pulmonary Tuberculoma and Solid Lung Adenocarcinoma in Tuberculosis-Endemic Areas

Background. Pulmonary tuberculoma can mimic lung malignancy and thereby pose a diagnostic dilemma to clinicians. The purpose of this study was to establish an accurate, convenient, and clinically practical model for distinguishing small-sized, noncalcified, solitary pulmonary tuberculoma from solid lung adenocarcinoma. Methods. Thirty-one patients with noncalcified, solitary tuberculoma and 30 patients with solid adenocarcinoma were enrolled. Clinical characteristics and CT morphological features of lesions were compared between the two groups. Multivariate logistic regression analyses were applied to identify independent predictors of pulmonary tuberculoma and lung adenocarcinoma. Receiver operating characteristic (ROC) analysis was performed to investigate the discriminating efficacy. Results. The mean age of patients with tuberculoma and adenocarcinoma was 46.8 ± 12.3 years (range, 28–64) and 61.1 ± 9.9 years (range, 41–77), respectively. No significant differences were observed concerning smoking history and smoking index, underlying disease, or tumor markers between the two groups. Univariate and multivariate analyses showed age and lobulation combined with pleural indentation demonstrated excellent discrimination. The sensitivity, specificity, accuracy, and the area under the ROC curve were 87.1%, 93.3%, 90.2%, and 0.956 (95% confidence interval (CI), 0.901–1.000), respectively. Conclusion. The combination of clinical characteristics and CT morphological features can be used to distinguish noncalcified, solitary tuberculoma from solid adenocarcinoma with high diagnostic performance and has a clinical application value.

"PET/CT Variants and Pitfalls in Lung Cancer and Mesothelioma"

2-deoxy-2-[¹⁸F]fluoro-D-glucose [¹⁸F]FDG-PET/CT represents the metabolic imaging of choice in various cancer types. Used either at diagnosis or during treatment response assessment, the modality allows for a more accurate definition of tumor extent compared to morphological imaging and is able to predict the therapeutic benefit earlier in time. Due to the aspecific uptake property of [¹⁸F]FDG there is an overlap of its distribution in normal and pathological conditions, which can make the interpretation of the imaging challenging. Lung and pleural neoplasia are no exception to this, thus acknowledging of possible pitfalls and artifacts are mandatory for image interpretation. While most pitfalls and artifacts are common for all indications with metabolic imaging with [¹⁸F]FDG-PET/CT, there are specific variants and pitfalls in lung cancer and malignant pleural mesothelioma. The aim of the present article is to shed light on the most frequent and relevant variants and pitfalls in [¹⁸F]FDG-PET/CT imaging in lung cancer and malignant pleural mesothelioma.

Cancer or Tuberculosis: A Comprehensive Review of the Clinical and Imaging Features in Diagnosis of the Confusing Mass

Confusing masses constitute a challenging clinical problem for differentiating between cancer and tuberculosis diagnoses. This review summarizes the major theories designed to identify factors associated with misdiagnosis, such as imaging features, laboratory tests, and clinical characteristics. Then, the clinical experiences regarding the misdiagnosis of cancer and tuberculosis are summarized. Finally, the main diagnostic points and differential diagnostic criteria are explored, and the characteristics of multimodal imaging and radiomics are summarized.

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.

Solitary pulmonary nodule imaging approaches and the role of optical fibre-based technologies

Solitary pulmonary nodules (SPNs) are a clinical challenge, given there is no single clinical sign or radiological feature that definitively identifies a benign from a malignant SPN. The early detection of lung cancer has a huge impact on survival outcome. Consequently, there is great interest in the prompt diagnosis, and treatment of malignant SPNs. Current diagnostic pathways involve endobronchial/transthoracic tissue biopsies or radiological surveillance, which can be associated with suboptimal diagnostic yield, healthcare costs and patient anxiety. Cutting-edge technologies are needed to disrupt and improve, existing care pathways. Optical fibre-based techniques, which can be delivered via the working channel of a bronchoscope or via transthoracic needle, may deliver advanced diagnostic capabilities in patients with SPNs. Optical endomicroscopy, an autofluorescence-based imaging technique, demonstrates abnormal alveolar structure in SPNs in vivo Alternative optical fingerprinting approaches, such as time-resolved fluorescence spectroscopy and fluorescence-lifetime imaging microscopy, have shown promise in discriminating lung cancer from surrounding healthy tissue. Whilst fibre-based Raman spectroscopy has enabled real-time characterisation of SPNs in vivo Fibre-based technologies have the potential to enable in situ characterisation and real-time microscopic imaging of SPNs, which could aid immediate treatment decisions in patients with SPNs. This review discusses advances in current imaging modalities for evaluating SPNs, including computed tomography (CT) and positron emission tomography-CT. It explores the emergence of optical fibre-based technologies, and discusses their potential role in patients with SPNs and suspected lung cancer.

Thoracic positron emission tomography: 18F-fluorodeoxyglucose and beyond

Ongoing technologic and therapeutic advancements in medicine are now testing the limits of conventional anatomic imaging techniques. The ability to image physiology, rather than simply anatomy, is critical in the management of multiple disease processes, especially in oncology. Nuclear medicine has assumed a leading role in detecting, diagnosing, staging and assessing treatment response of various pathologic entities, and appears well positioned to do so into the future. When combined with computed tomography (CT) or magnetic resonance imaging (MRI), positron emission tomography (PET) has become the sine quo non technique of evaluating most solid tumors especially in the thorax. PET/CT serves as a key imaging modality in the initial evaluation of pulmonary nodules, often obviating the need for more invasive testing. PET/CT is essential to staging and restaging in bronchogenic carcinoma and offers key physiologic information with regard to treatment response. A more recent development, PET/MRI, shows promise in several specific lung cancer applications as well. Additional recent advancements in the field have allowed PET to expand beyond imaging with ¹⁸F-flurodeoxyglucose (FDG) alone, now with the ability to specifically image certain types of cell surface receptors. In the thorax this predominantly includes ⁶⁸Ga-DOTATATE which targets the somatostatin receptors abundantly expressed in neuroendocrine tumors, including bronchial carcinoid. This receptor targeted imaging technique permits targeting these tumors with therapeutic analogues such as ¹⁷⁷Lu labeled DOTATATE. Overall, the proper utilization of PET in the thorax has the ability to directly impact and improve patient care.

Recent and Current Advances in FDG-PET Imaging within the Field of Clinical Oncology in NSCLC: A Review of the Literature

Lung cancer is the leading cause of cancer-related deaths around the world, the most common type of which is non-small-cell lung cancer (NSCLC). Computed tomography (CT) is required for patients with NSCLC, but often involves diagnostic issues and large intra- and interobserver variability. The anatomic data obtained using CT can be supplemented by the metabolic data obtained using fluorodeoxyglucose F 18 (FDG) positron emission tomography (PET); therefore, the use of FDG-PET/CT for staging NSCLC is recommended, as it provides more accuracy than either modality alone. Furthermore, FDG-PET/magnetic resonance imaging (MRI) provides useful information on metabolic activity and tumor cellularity, and has become increasingly popular. A number of studies have described FDG-PET/MRI as having a high diagnostic performance in NSCLC staging. Therefore, multidimensional functional imaging using FDG-PET/MRI is promising for evaluating the activity of the intratumoral environment. Radiomics is the quantitative extraction of imaging features from medical scans. The chief advantages of FDG-PET/CT radiomics are the ability to capture information beyond the capabilities of the human eye, non-invasiveness, the (virtually) real-time response, and full-field analysis of the lesion. This review summarizes the recent advances in FDG-PET imaging within the field of clinical oncology in NSCLC, with a focus on surgery and prognostication, and investigates the site-specific strengths and limitations of FDG-PET/CT. Overall, the goal of treatment for NSCLC is to provide the best opportunity for long-term survival; therefore, FDG-PET/CT is expected to play an increasingly important role in deciding the appropriate treatment for such patients.

Predictive value of interim 18F-FDG-PET in patients with non-small cell lung cancer treated with definitive radiation therapy

PURPOSE

We evaluated that early metabolic response determined by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) during radiotherapy (RT), predicts outcomes in non-small cell lung cancer.

MATERIAL AND METHODS

Twenty-eight patients evaluated using pretreatment 18F-FDG-PET/CT (PETpre) and interim 18F-FDG-PET/CT (PETinterim) after 11 fractions of RT were retrospectively reviewed. Maximum standardized uptake value (SUVmax) was calculated for primary lesion. Predictive value of gross tumor volume (ΔGTV) and SUVmax (ΔSUVmax) changes was evaluated for locoregional control (LRC), distant failure (DF), and overall survival (OS). Metabolic responders were patients with ΔSUVmax >40%.

RESULTS

Metabolic responders showed better trends in 1-year LRC (90.9%) than non-responders (47.1%) (p = 0.086). Patients with large GTVpre (≥120 cc) demonstrated poor LRC (hazard ratio 4.14, p = 0.022), while metabolic non-responders with small GTVpre (<120 cc) and metabolic responders with large GTVpre both had 1-year LRC rates of 75.0%. Reduction of 25% in GTV was not associated with LRC; however, metabolic responders without a GTV response showed better 1-year LRC (83.3%) than metabolic non-responders with a reduction in GTV (42.9%). Metabolic responders showed lower 1-year DF (16.7%) than non-responders (50.0%) (p = 0.025). An ΔSUVmax threshold of 40% yielded accuracy of 64% for predicting LRC, 75% for DF, and 54% for OS. However, ΔGTV > 25% demonstrated inferior diagnostic values than metabolic response.

CONCLUSIONS

Changes in tumor metabolism diagnosed using PETinterim during RT better predicted treatment responses, recurrences, and prognosis than other factors historically used.

Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor

Background

This study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images.

Methods

In this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test.

Results

A total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC ≥ 0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves (AUC) of these features were 0.731 ± 0.075, 0.717, 0.748 ± 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features (Child features).

Conclusion

Radiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.

Prognostic Impact of Intratumoral Heterogeneity Based on Fractal Geometry Analysis in Operated NSCLC Patients

PURPOSE

To determine the heterogeneity of glucose uptake applying fractal analysis on positron emission tomography/computed tomography (PET/CT) with 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) images in patients with non-small cell lung carcinoma (NSCLC) before surgery, and to assess whether this heterogeneity was associated with disease-free survival (DFS).

PROCEDURES

[¹⁸F]FDG PET/CT scans of 113 patients' prior surgery were retrospectively revised. PET DICOM images were analyzed for fractal geometry using a ad hoc software to automatically determine the following indexes: (a) mean intensity value (MIV), (b) standard deviation (SD), (c) relative dispersion (RD), (d) three-dimensional (3D) histogram of the fractal dimension (3D HIST FR DIM), and (e) fractal dimension in 3D (3D-FD). All the fractal indexes were subsequently compared with metabolic parameters and disease-free survival (DFS).

RESULTS

We found a significant correlation between 3D-FD and SUVmax, SUVmean, MTV, and TLG. Additionally, positive correlations between MIV, SD, and all metabolic parameters were also detected. Patients with high 3D-FD tumor (≥ 1.62) showed significantly higher values of SUVmax, SUVmean, MTV, and TLG than those with lower 3D-FD. In univariate analysis, median 3D-FD and median TLG were significantly associated with DFS (p = 0.04 and p = 0.03, respectively). These findings were confirmed on log-rank test. On multivariate analysis, among age, stage disease, histotype, 3D-FD, and metabolic parameters, only 3D-FD was identified as independent prognostic factor for DFS (p = 0.032; HR 0.418, 95 % CI 0.189-0.926). 3D-FD was different between adenocarcinoma and squamous cell carcinoma (1.60 versus 1.88, p = 0.014), and 3D-FD value was found higher in advanced stage disease.

CONCLUSIONS

Metabolic heterogeneity determined applying fractal principles on PET images can be considered as a novel imaging biomarker for survival in patients with NSCLC.

Signaling pathways and clinical application of RASSF1A and SHOX2 in lung cancer

BACKGROUND

An increasing number of studies have focused on the early diagnostic value of the methylation of RASSF1A and SHOX2 in lung cancer. However, the intricate cellular events related to RASSF1A and SHOX2 in lung cancer are still a mystery. For researchers and clinicians aiming to more profoundly understand the diagnostic value of methylated RASSF1A and SHOX2 in lung cancer, this review will provide deeper insights into the molecular events of RASSF1A and SHOX2 in lung cancer.

METHODOLOGY

We searched for relevant publications in the PubMed and Google Scholar databases using the keywords "RASSF1A", "SHOX2" and "lung cancer" etc. First, we reviewed the RASSF1A and SHOX2 genes, from their family structures to the functions of their basic structural domains. Then we mainly focused on the roles of RASSF1A and SHOX2 in lung cancer, especially on their molecular events in recent decades. Finally, we compared the value of measuring RASSF1A and SHOX2 gene methylation with that of the common methods for the diagnosis of lung cancer patients.

RESULTS

The RASSF1A and SHOX2 genes were confirmed to be regulators or effectors of multiple cancer signaling pathways, driving tumorigenesis and lung cancer progression. The detection of RASSF1A and SHOX2 gene methylation has higher sensitivity and specificity than other commonly used methods for diagnosing lung cancer, especially in the early stage.

CONCLUSIONS

The RASSF1A and SHOX2 genes are critical for the processes of tumorigenesis, development, metastasis, drug resistance, and recurrence in lung cancer. The combined detection of RASSF1A and SHOX2 gene methylation was identified as an excellent method for the screening and surveillance of lung cancer that exhibits high sensitivity and specificity.

Functional and metabolic imaging in transthoracic biopsies guided by computed tomography

OBJECTIVES

CT-guided biopsy of indeterminate lung lesions sometimes provides insufficient histological results due to tumor necrosis. Functional and metabolic methods such as DWI-MR and PET-CT may help by directing sample collection to a lesion area of greater biological representativeness. The objective is to evaluate the histopathological results based on findings on ADC and SUV levels in lung lesions suspected for primary cancer.

METHODS

Tissue samples were evaluated after undergoing biopsies guided by either DWI-MR or PET-CT findings. In each patient, sample collection from two lesion areas was guided by local ADC and SUV. Values were used to define areas of low vs. high suspicion for cancer.

RESULTS

Patients who underwent DWI-MR had median lesion size of 78.0 mm. Areas of higher suspicion (HSA) had a median ADC of 1.1 × 10^-3 mm²/s, while areas of lower suspicion (LSA) had median ADC of 1.8 × 10^-3 mm²/s (p = 0.0001). All HSA samples and 71.43% of LSA samples were positive for cancer (p = 0.0184). Patients who performed PET-CT had median lesion size of 61.0 mm. Median SUV was 7.1 for HSA and 3.9 for LSA (p = 0.0002). Positivity for cancer was observed in 76.9% of samples for both HSA and LSA (p = 0.0522).

CONCLUSION

Use of DWI-MR and PET-CT showed that tumors are functional and metabolically heterogeneous and that this heterogeneity has implications for histopathological diagnosis.

KEY POINTS

• Lung cancer is heterogeneous regarding functional and metabolic imaging. • Tumor heterogeneity may have implications in histopathological diagnosis. • Intralesional lower levels of ADC target highly suspected areas with a significant improvement in lung cancer diagnosis.

Role of FDG PET/CT in the Eighth Edition of TNM Staging of Non-Small Cell Lung Cancer

Lung cancer is the leading cause of cancer-related mortality in the United States, and accurate staging plays a vital role in determining prognosis and treatment. The recently revised eighth edition of the TNM staging system for lung cancer defines new T and M descriptors and updates stage groupings on the basis of substantial differences in survival. There are new T descriptors that are based on the findings at histopathologic examination, and T descriptors are reassigned on the basis of tumor size and extent. No changes were made to the N descriptors in the eighth edition of the TNM staging of lung cancer, because the four N categories that are based on the location of the diseased nodes can be used to consistently predict prognosis. The eighth edition includes a new M1b descriptor for patients with a single extrathoracic metastatic lesion in a single organ (M1b), because they have better survival and different treatment options, compared with those with multiple extrathoracic lesions (M1c). Examination with fluorine 18 fluorodeoxyglucose (FDG) PET/CT is the standard of care and is an integral part of the clinical staging of patients with lung cancer. To provide the treating physicians with accurate staging information, radiologists and nuclear medicine physicians should be aware of the updated classification system and should be cognizant of the site-specific strengths and limitations of FDG PET/CT. In this article, the eighth edition of the TNM staging system is reviewed, as well as the role of FDG PET/CT in the staging of non-small cell lung carcinoma. ^©RSNA, 2018.

The value of 18F-FDG PET/CT in the diagnosis of different size of solitary pulmonary nodules

This study aimed to evaluate the diagnostic value of F-fluorodeoxyglucose (F-FDG) positron emission tomography/computed tomography (PET/CT) for solitary pulmonary nodules (SPNs) with different diameters.One hundred eighty two consecutive patients with SPN who underwent F-FDG PET/CT examination were retrospectively studied. Patients were categorized into 3 groups according to the diameter of nodules: Group A with the diameter of greater than or equal to 6 mm and less than or equal to 10 mm; Group B with diameter greater than 10 mm and less than or equal to 20 mm; Group C with diameter greater than 20 mm and less than or equal to 30 mm. The efficiency of PET/CT, PET and CT in the diagnosis of benign and malignant SPNs and different subgroup of SPNs was calculated. Receiver operating characteristic curves (ROCs) were drawn and area under the curves (AUCs) were compared between different groups.The age, diameter, mean standardized uptake value (SUVmean) and maximum standardized uptake value (SUVmax) of benign and malignant nodules were significantly different (P < .05). For overall SPNs, the sensitivity, specificity, accuracy, PPV, and NPV of PET/CT were 98.35%, 77.05%, 91.21%, 89.47%, and 95.92%, respectively. The AUC of PET/CT was significantly larger than that of SUVmean, SUVmax, and CT (P < .05). For different size of SPNs, the AUC of PET/CT in group A was higher than that in group B and group C, but there was no significant difference with CT (P > .05). In group B, the accuracy of PET/CT in the diagnosis of SPN was significantly higher than that of CT (P < .05).F-FDG PET/CT demonstrated excellent performance in identifying different size of SPNs, especially for those with diameter between 11 and 20 mm, the diagnostic value of F-FDG PET/CT is significantly higher than other methods.

Adaptive metabolic pattern biomarker for disease monitoring and staging of lung cancer with liquid biopsy

In this manuscript, we demonstrate the applicability of a metabolic liquid biopsy for the monitoring and staging of patients with lung cancer. This method provides an unbiased detection strategy to establish a more precise correlation between CTC quantification and the actual burden of disease, therefore improving the accuracy of staging based on current imaging techniques. Also, by applying statistical analysis techniques and probabilistic models to the metabolic status and distribution of peripheral blood mononuclear cell (PBMC) populations "perturbed" by the presence of CTCs, a new category of adaptive metabolic pattern biomarker (AMPB) is described and unambiguously correlated to the different clinical stages of the patients. In fact, this strategy allows for classification of different categories of disease within a single stage (stage IV) before computed tomography (CT) and positron emission tomography (PET) scans and with lower uncertainty.

The Contribution of Fluorine 18F-FDG PET/CT to Lung Cancer Diagnosis, Staging and Treatment Planning

Objective:

Lung cancer is the most common cause of cancer-related death throughout the world, and the correct choice of treatment based on early diagnosis and staging increases the chance of survival. The present study aims to investigate the contribution of fluorine 18-fluorodeoxyglucose-positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) to the management of lung cancer.

Methods:

In this study, 50 patients who underwent ¹⁸F-FDG PET/CT for lung cancer diagnosis and staging between February 2012 and February 2014 were included. The maximum standardized uptake value (SUV_max) of the primary lung lesion along with other findings of ¹⁸F-FDG PET/CT and the results of histopathologic and conventional examinations were evaluated retrospectively. The mean survival time of patients was determined, and the findings were compared by using statistical methods.

Results:

Histopathologic examinations revealed 51 lung cancers in 50 patients. The sensitivity, accuracy and positive predictive value of ¹⁸F-FDG PET/CT in detecting primary malignancy were 94%, 94%, 100%, respectively. Adenocarcinoma (n=23, 16.8±13.5) and squamous cell carcinoma (n=15, 17.9±5.6) did not differ significantly regarding their mean SUV_max values (p=0.2). A statistically significant positive correlation (r=0.4) was identified between tumor size and SUV_max value for 51 tumors (p=0.002). The ¹⁸F-FDG PET/CT result was true negative in nine, false positive in six, true positive in two, and false negative in four patients who underwent histopathologic evaluation of their lymph nodes. The ¹⁸F-FDG PET/CT changed treatment planning in 34% of the patients. No significant relationship was identified between SUV_max value of the tumor and patient survival in patients (p=0.118).

Conclusion:

The present study concluded that PET/CT was an efficient method in the diagnosis and staging of lung cancer since it provided useful information in addition to conventional methods. It was also observed that PET/CT scanning resulted in a change in therapeutic plans in the majority of patients. However, there was no statistically significant relationship between survival and the SUV_max of the primary mass.

The role of positron emission tomography in the diagnosis, staging and response assessment of non-small cell lung cancer

Lung cancer is a common disease and the leading cause of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Following diagnosis of lung cancer, accurate staging is essential to guide clinical management and inform prognosis. Positron emission tomography (PET) in conjunction with computed tomography (CT)-as PET-CT has developed as an important tool in the multi-disciplinary management of lung cancer. This article will review the current evidence for the role of ¹⁸F-fluorodeoxyglucose (FDG) PET-CT in NSCLC diagnosis, staging, response assessment and follow up.