PET/CT imaging in different types of lung cancer: an overview

Does High Standard Uptake Value on Positron Emission Tomography Preclude Sublobar Resection in Stage IA Non-Small Cell Lung Cancer ≤2 cm?

BACKGROUND

Recent randomized trials have shown equivalent survival after sublobar resection vs lobectomy in patients with clinical stage IA non-small cell lung cancer (NSCLC) ≤2 cm. High maximum standard uptake value (SUVmax) is a known risk factor in NSCLC, yet limited data exist on whether a high SUV should preclude a sublobar resection. This study aimed to determine whether there is an association between SUVmax and survival based on the extent of parenchymal resection.

METHODS

A retrospective review of a prospectively maintained institutional database was conducted to identify patients with clinical stage IA NSCLC ≤2 cm (2011-2020) treated with sublobar resection or lobectomy. The primary outcome was cancer-specific survival (CSS). Secondary outcomes were overall survival and disease-free survival.

RESULTS

There were 543 patients identified; 36.8% had sublobar resection and 63.2% had lobectomy. Baseline characteristics were similar. Patients who had sublobar resection had significantly worse Eastern Cooperative Oncology Group performance status and higher rates of comorbidities. The 5-year CSS, overall survival, and disease-free survival for the whole cohort were similar between sublobar resection and lobectomy. A receiver operating characteristic curve estimated the SUVmax cutoff point to be 4.15. For the whole cohort, patients with SUVmax >4.15 had worse CSS compared with SUVmax ≤4.15. However, there was no significant difference in 5-year CSS after sublobar resection vs lobectomy in patients with SUVmax ≤4.15 (98% in both groups; P = .77) or patients with SUVmax >4.15 (90% vs 94%, respectively; P = .12).

CONCLUSIONS

SUVmax may not be a useful clinical determinant of the extent of parenchymal resection in patients with cT1 N0 NSCLC ≤2 cm. Patients treated by sublobar resection had comparable survival to lobectomy, irrespective of positron emission tomography avidity.

Deep learning object detection-based early detection of lung cancer

The early diagnosis and accurate classification of lung cancer have a critical impact on clinical treatment and patient survival. The rise of artificial intelligence technology has led to breakthroughs in medical image analysis. The Lung-PET-CT-Dx public dataset was used for the model training and evaluation. The performance of the You Only Look Once (YOLO) series of models in the lung CT image object detection task is compared in terms of algorithms, and different versions of YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 are examined for lung cancer detection and classification. The experimental results indicate that the prediction results of YOLOv8 are better than those of the other YOLO versions, with a precision rate of 90.32% and a recall rate of 84.91%, which proves that the model can effectively assist physicians in lung cancer diagnosis and improve the accuracy of disease localization and identification.

Demystifying the Role of Immuno PET-CT in Non-Small Cell Lung Cancer: Clinical Value and Research Trends

The management of Lung cancer, especially non-small cell lung cancer has undergone a paradigm shift recently with the advent of new treatment approaches including focused radiotherapy as well as evolution of a newer class of immunotherapy agents. Treatment efficacy and survival rates have improved and it is now even more important that patients are selected for appropriate interventions on the basis of a comprehensive assessment including a range of imaging as well as in-vitro tests such as immunohistochemistry. A new class of tracers targeting programmed cell death such as PD1 and PDL1 (broadly classed as Immuno PET) are being increasingly used in the molecular characterisation of patients deemed resistant to standard treatment approaches and being considered for additional interventions such as immunotherapy. In this review, we review the latest evidence in the field and propose a summary of clinical usefulness and provide a review of the research trends in this exciting and evolving field.

Total Body Positron Emission Tomography/Computed Tomography: Current Status in Oncology

Positron Emission Tomography (PET) is a crucial imaging modality in oncology, providing functional insights by detecting metabolic activity in tissues. Total-body (TB) PET and large field-of-view PET have emerged as advanced techniques, offering whole-body imaging in a single acquisition. TB PET enables simultaneous imaging from head to toe, providing comprehensive information on tumor distribution, metastasis, and treatment response. This is particularly valuable in oncology, where metastatic spread often requires evaluation of multiple body areas. By covering the entire body, TB PET improves diagnostic accuracy, reduces scan time, and increases patient comfort. Furthermore, these new tomographs offer a marked increase in sensitivity, thanks to their ability to capture a larger volume of data simultaneously. This heightened sensitivity enables the detection of smaller lesions and more subtle metabolic changes, improving diagnostic accuracy in the early stages of cancer or in the evaluation of minimal residual disease. Moreover, the increased sensitivity allows for lower radiotracer doses without compromising image quality, reducing patient exposure to radiation or very quick acquisitions. Another significant advantage is the possibility of dynamic acquisitions, which allow for continuous monitoring of tracer kinetics over time. This provides critical information about tissue perfusion, metabolism, and receptor binding in real time. Dynamic imaging is particularly useful for assessing treatment response in oncology, as it enables the evaluation of tumor behavior over a period rather than a single static snapshot, offering insights into tumor aggressiveness and potential therapeutic targets. This review is focused on the current applications of TB and large field-of-view PET scanners in oncology.

Efficacy of 18FDG-PET/CT in Detecting Synchronous Malignancies in Patients With Head and Neck Cancer: A Systematic Review and Meta-analysis

OBJECTIVE

To assess the diagnostic accuracy of fluorine-18 fluorodeoxyglucose positron emission tomography and computed tomography (18FDG-PET/CT) in detecting second primary malignancies (SPMs) in patients with treatment naïve head and neck squamous cell carcinoma (HNSCC).

DATA SOURCES

Medline, Embase, Cochrane Library, and Scopus searched from 1946 to December 2022.

REVIEW METHODS

Studies reporting the performance of 18FDG-PET/CT in patients with treatment-naïve, index HNSCC for detection of SPMs were included. The reference standard was histopathology, clinical follow-up over the duration of study, and other imaging modalities. Multiple investigators completed depth full-text analysis. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Methodologic and diagnostic accuracy data were abstracted independently by multiple investigators. Risk of bias assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies tool independently. Bivariate random-effects model meta-analysis and multivariable meta-regression modeling were used.

RESULTS

Seventeen studies examining 4624 patients with a total of 475 SPMs were included in the final analysis. Eleven studies were found to be at low risk for bias, while the rest were in the high-risk category. 18FDG-PET/CT demonstrated pooled sensitivity and specificity of 0.73 (95% confidence interval [CI]: 0.49-0.88) and 0.99 (95% CI: 0.98-1.00) in detecting SPMs. Further subsite analysis revealed varied diagnostic performance across different anatomical regions, with sensitivity and specificity of esophageal SPMs being 0.47 (0.30-0.64) and 0.99 (0.98-1.00), and sensitivity and specificity of 0.86 (0.73-0.94) and 0.99 (0.98-1.00) for head and neck SPMs. Finally, this imaging modality showed sensitivity and specificity of 0.92 (0.84-0.96) and 0.99 (0.98-1.00) for lung SPMs.

CONCLUSION

The findings of this study suggest varied accuracy of 18FDG-PET/CT in detecting SPMs during initial workup for HNSCC, highlighting the importance of screening modalities such as esophagoscopy in high-risk patients.

Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome

PURPOSE

Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome.

METHODS

[18F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score.

RESULTS

Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001).

CONCLUSIONS

Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome.

Effect of FDG PET-CT for Staging and Radiotherapy Planning - A Comparison of Cohorts From Two Randomized Trials of Thoracic Radiotherapy in Limited-Stage SCLC

Introduction

18F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) is recommended for staging and defining target volume in limited-stage SCLC, though the impact on outcomes compared with CT staging and elective nodal irradiation (ENI) is not well documented. We analyzed patients receiving 45 Gy/30 fractions in two randomized trials of thoracic radiotherapy (TRT) in limited-stage SCLC (HAST and THORA trials) to evaluate whether PET-CT for staging and radiotherapy planning reduces radiotoxicity and improves survival.

Methods

Patients in HAST were staged with CT of the thorax and upper abdomen and brain magnetic resonance imaging of the brain. Patients in THORA were staged with PET-CT in addition. All patients were to receive four courses of platinum/etoposide chemotherapy and concurrent TRT starting three to four weeks after the first chemotherapy course. In HAST, target volumes included pathological lesions on CT plus ENI of lymph node stations 4-7 (bilateral). In THORA, target volumes were limited to PET-CT-positive lesions (selective nodal irradiation [SNI]).

Results

A total of 149 patients were included (PET-CT/SNI: n = 76, CT/ENI: n=73); the median age was 64 years, 56% were women, 85% had PS 0 to 1, and 81% had stage III disease. The PET-CT/SNI group experienced less grade 3-4 esophagitis (18% versus 33%, p = 0.043), less grade >=1 pneumonitis (5% versus 16%, p = 0.028), and less dysphagia after TRT (mean scores on European Organisation for Research and Treatment of Cancer 13-item lung cancer module: 45 versus 72). There was no difference in median overall survival (24 versus 25 mo, p = 0.59) or progression-free survival (11 versus 11 mo, p = 0.23).

Conclusions

Using PET-CT for staging and target volume definition of TRT reduces acute radiotoxicity but does not improve overall or progression-free survival in limited-stage SCLC.

Diagnosis of contralateral rare pulmonary cavity metastasis after lung squamous cell carcinoma surgery by electromagnetic navigation: one case report and review of the literature

Background

Lung cancer associated with cystic airspaces is a rare disease, and a rare imaging performance of non-small cell lung cancer. Due to the lack of conventional diagnosis methods, it is difficult to rely on imaging diagnosis. Therefore, the definitive diagnosis of these neoplastic lesions remains challenging.

Case presentation

We summarize the follow-up and diagnosis of a rare cystic airspaces lung metastatic carcinoma in an elderly man with annular density shadow in the right inferior lobe 2 years after surgery for squamous cell carcinoma in the left inferior lobe.

Results

During the follow-up of the patient, after the lesion of the lower lobe of the right lung was enlarged, the structural and imaging characteristics were identified, and a special method was selected, namely biopsy of the lesion under the electromagnetic navigation bronchoscope, for clear diagnosis and subsequent treatment.

Conclusion

For pulmonary cystic airspaces, it is important to correctly identify their imaging features. Because of the possibility of malignancy, it is essential to stop the radiological study in time and to acquire the pathological diagnosis by an appropriate method.

The effects of airway disease on the deposition of inhaled drugs

INTRODUCTION

The deposition of inhaled medications is the first step in the pulmonary pharmacokinetic process to produce a therapeutic response. Not only lung dose but more importantly the distribution of deposited drug in the different regions of the lung determines local bioavailability, efficacy, and clinical safety. Assessing aerosol deposition patterns has been the focus of intense research that combines the fields of physics, radiology, physiology, and biology.

AREAS COVERED

The review covers the physics of aerosol transport in the lung, experimental, and in-silico modeling approaches to determine lung dose and aerosol deposition patterns, the effect of asthma, chronic obstructive pulmonary disease, and cystic fibrosis on aerosol deposition, and the clinical translation potential of determining aerosol deposition dose.

EXPERT OPINION

Recent advances in in-silico modeling and lung imaging have enabled the development of realistic subject-specific aerosol deposition models, albeit mainly in health. Accurate modeling of lung disease still requires additional refinements in existing imaging and modeling approaches to better characterize disease heterogeneity in peripheral airways. Nevertheless, recent patient-centric innovation in inhaler device engineering and the incorporation of digital technology have led to more consistent lung deposition and improved targeting of the distal airways, which better serve the clinical needs of patients.

Interpreting Lung Cancer Screening CTs: Practical Approach to Lung Cancer Screening and Application of Lung-RADS

Lung cancer screening via low-dose computed tomography (CT) reduces mortality from lung cancer, and eligibility criteria have recently been expanded to include patients aged 50 to 80 with at least 20 pack-years of smoking history. Lung cancer screening CTs should be interepreted with use of Lung Imaging Reporting and Data System (Lung-RADS), a reporting guideline system that accounts for nodule size, density, and growth. The revised version of Lung-RADS includes several important changes, such as expansion of the definition of juxtapleural nodules, discussion of atypical pulmonary cysts, and stepped management for suspicious nodules. By using Lung-RADS, radiologists and clinicians can adopt a uniform approach to nodules detected during CT lung cancer screening and reduce false positives.

Predicting lung cancer's metastats' locations using bioclinical model

Background

Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches.

Methods

This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients.

Findings

The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment.

Interpretation

This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.

Deep Multimodal Fusion of Data With Heterogeneous Dimensionality via Projective Networks

The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the benefits of multimodal fusion for automatic segmentation and classification using deep learning-based methods. However, current segmentation methods are limited to fusion of modalities with the same dimensionality (e.g., 3D + 3D, 2D + 2D), which is not always possible, and the fusion strategies implemented by classification methods are incompatible with localization tasks. In this work, we propose a novel deep learning-based framework for the fusion of multimodal data with heterogeneous dimensionality (e.g., 3D + 2D) that is compatible with localization tasks. The proposed framework extracts the features of the different modalities and projects them into the common feature subspace. The projected features are then fused and further processed to obtain the final prediction. The framework was validated on the following tasks: segmentation of geographic atrophy (GA), a late-stage manifestation of age-related macular degeneration, and segmentation of retinal blood vessels (RBV) in multimodal retinal imaging. Our results show that the proposed method outperforms the state-of-the-art monomodal methods on GA and RBV segmentation by up to 3.10% and 4.64% Dice, respectively.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

18F-FDG PET/CT anatomic and metabolic guidance in CT-guided lung biopsies

PURPOSE

To evaluate the role of Fluorine-18 fluorodeoxyglucose (18F-FDG) PET/CT as a metabolic guide in increasing the accuracy, diagnostic yield and safety of CT-guided percutaneous needle lung biopsy (PNB).

METHODS AND MATERIALS

Retrospective analysis of 340 consecutive patients with suspicious lung nodules, masses or extensive disease that underwent lung biopsy over a 3-year period. Patients were divided into three groups; those that had PET/CT prior to the biopsy, those that had PET-CT following the biopsy and those who did not undergo PET-CT. Correlation was made with the histopathological result.

RESULTS

353 PNBs were performed (median lesion size 30 mm, 7-120 mm) with overall diagnostic rate of 83.9 % (95.8 % malignant). Biopsy success rate was 88.8 % with PET-CT pre-PNB, versus 78.9 % of 175 PNB without PET-CT upfront (p < 0.01 Fisher exact test). Correct targeting to PET-CT-maximum activity area (MAA) was present in 87.1 %. Biopsy success rate was 88.8 % for PNBs targeting the PET-CT-MAA region and only 52.8 % for PNBs not targeting the PET-CT-MAA (p < 0.0001). PET-CT pre-PNB had higher rates of PET-CT-MAA targeting compared to PET-CT post PNB (91.0 % v 80.0 %, p = 0.01). The availability of PET-CT before the PNB lead to significantly increased biopsy success rates in patients with a mass (OR:7.01p = 0.004), compared to a nodule (p = 0.498) or multiple nodules (p = 0.163). Patients with a PET-CT pre-PNB underwent fewer PNB passes (mean 2.6 v 3.1, p < 0.0001 Mann Whitney U). Serious complications were less common in PET-CT pre-PNB group (4.5 % v 10.9 %, p < 0.05). Pre-PNB PET-CT performance improvement applied to all 3 radiologists and was greatest for masses and infiltrative abnormalities.

CONCLUSION

Metabolic information provided by 18F-FDG PET/CT and PNB localisation to the PET-CT maximum activity region is associated with higher diagnostic biopsy rates especially in masses and appears to account for improved performance, less needle passes and complications when available pre-biopsy.

Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules

Background

In this study, we developed and validated machine learning (ML) models by combining radiomic features extracted from magnetic resonance imaging (MRI) with clinicopathological factors to assess pulmonary nodule classification for benign malignant diagnosis.

Methods

A total of 333 consecutive patients with pulmonary nodules (233 in the training cohort and 100 in the validation cohort) were enrolled. A total of 2,824 radiomic features were extracted from the MRI images (CE T1w and T2w). Logistic regression (LR), Naïve Bayes (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers were used to build the predictive models, and a radiomics score (Rad-score) was obtained for each patient after applying the best prediction model. Clinical factors and Rad-scores were used jointly to build a nomogram model based on multivariate logistic regression analysis, and the diagnostic performance of the five prediction models was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

A total of 161 women (48.35%) and 172 men (51.65%) with pulmonary nodules were enrolled. Six important features were selected from the 2,145 radiomic features extracted from CE T1w and T2w images. The XGBoost classifier model achieved the highest discrimination performance with AUCs of 0.901, 0.906, and 0.851 in the training, validation, and test cohorts, respectively. The nomogram model improved the performance with AUC values of 0.918, 0.912, and 0.877 in the training, validation, and test cohorts, respectively.

Conclusion

MRI radiomic ML models demonstrated good nodule classification performance with XGBoost, which was superior to that of the other four models. The nomogram model achieved higher performance with the addition of clinical information.

Deep learning-based dynamic PET parametric Ki image generation from lung static PET

OBJECTIVES

PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric K_i provides better quantification and improve specificity for cancer detection. However, parametric imaging is difficult to implement clinically due to the long acquisition time (~ 1 h). We propose a dynamic parametric imaging method based on conventional static PET using deep learning.

METHODS

Based on the imaging data of 203 participants, an improved cycle generative adversarial network incorporated with squeeze-and-excitation attention block was introduced to learn the potential mapping relationship between static PET and K_i parametric images. The image quality of the synthesized images was qualitatively and quantitatively evaluated by using several physical and clinical metrics. Statistical analysis of correlation and consistency was also performed on the synthetic images.

RESULTS

Compared with those of other networks, the images synthesized by our proposed network exhibited superior performance in both qualitative and quantitative evaluation, statistical analysis, and clinical scoring. Our synthesized K_i images had significant correlation (Pearson correlation coefficient, 0.93), consistency, and excellent quantitative evaluation results with the K_i images obtained in standard dynamic PET practice.

CONCLUSIONS

Our proposed deep learning method can be used to synthesize highly correlated and consistent dynamic parametric images obtained from static lung PET.

KEY POINTS

• Compared with conventional static PET, dynamic PET parametric K_i imaging has been shown to provide better quantification and improved specificity for cancer detection. • The purpose of this work was to develop a dynamic parametric imaging method based on static PET images using deep learning. • Our proposed network can synthesize highly correlated and consistent dynamic parametric images, providing an additional quantitative diagnostic reference for clinicians.

Systematic Review of Tumor Segmentation Strategies for Bone Metastases

Purpose: To investigate the segmentation approaches for bone metastases in differentiating benign from malignant bone lesions and characterizing malignant bone lesions. Method: The literature search was conducted in Scopus, PubMed, IEEE and MedLine, and Web of Science electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 77 original articles, 24 review articles, and 1 comparison paper published between January 2010 and March 2022 were included in the review. Results: The results showed that most studies used neural network-based approaches (58.44%) and CT-based imaging (50.65%) out of 77 original articles. However, the review highlights the lack of a gold standard for tumor boundaries and the need for manual correction of the segmentation output, which largely explains the absence of clinical translation studies. Moreover, only 19 studies (24.67%) specifically mentioned the feasibility of their proposed methods for use in clinical practice. Conclusion: Development of tumor segmentation techniques that combine anatomical information and metabolic activities is encouraging despite not having an optimal tumor segmentation method for all applications or can compensate for all the difficulties built into data limitations.

Diagnostic performance of diffusion-weighted imaging versus 18F-FDG PET/CT in differentiating pulmonary lesions: an updated meta-analysis of comparative studies

OBJECTIVE

To compare the diagnostic accuracy of diffusion-weighted imaging (DWI) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for differentiating pulmonary nodules and masses.

METHODS

We systematically searched six databases, including PubMed, EMBASE, the Cochrane Library, and three Chinese databases, to identify studies that used both DWI and PET/CT to differentiate pulmonary nodules. The diagnostic performance of DWI and PET/CT was compared and pooled sensitivity and specificity were calculated along with 95% confidence intervals (CIs). The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess the quality of the included studies, and STATA 16.0 software was utilized to perform statistical analysis.

RESULTS

Overall, 10 studies that enrolled a total of 871 patients with 948 pulmonary nodules were included in this meta-analysis. DWI had greater pooled sensitivity (0.85 [95% CI 0.77-0.90]) and specificity (0.91 [95% CI 0.82-0.96]) than PET/CT (sensitivity, 0.82 [95% CI 0.70-0.90]); specificity, (0.81, [95% CI 0.72-0.87]). The area under the curve of DWI and PET/CT were 0.94 (95% CI 0.91-0.96) and 0.87 (95% CI 0.84-0.90) (Z = 1.58, P > 0.05), respectively. The diagnostic odds ratio of DWI (54.46, [95% CI 17.98-164.99]) was superior to that of PET/CT (15.77, [95% CI 8.19-30.37]). The Deeks' funnel plot asymmetry test showed no publication bias. The Spearman correlation coefficient test revealed no significant threshold effect. Lesion diameter and reference standard could be potential causes for the heterogeneity of both DWI and PET/CT studies, and quantitative or semi-quantitative parameters used would be a potential source of bias for PET/CT studies.

CONCLUSION

As a radiation-free technique, DWI may have similar performance compare with PET/CT in differentiating malignant pulmonary nodules or masses from benign ones.

[68Ga]Ga-Pentixafor PET/CT imaging for in vivo CXCR4 receptor mapping in different lung cancer histologic sub-types: correlation with quantitative receptors' density by immunochemistry techniques

PURPOSE

In vivo CXCR4 receptor quantification in different lung cancer (LC) sub-types using [⁶⁸Ga]Ga-Pentixafor PET/CT and to study correlation with quantitative CXCR4 receptors' tissue density by immunochemistry analyses.

METHODS

[⁶⁸Ga]Ga-Pentixafor PET/CT imaging was performed prospectively in 94 (77 M: 17F, mean age 60.1 ± 10.1 years) LC patients. CXCR4 receptors' expression on lung mass in all the patients was estimated by immunohistochemistry (IHC) and fluorescence-activated cell sorting (FACS) analyses. SUV_max on PET, intensity score on IHC, and mean fluorescence index (MFI) on FACS analyses were measured.

RESULTS

A total of 75/94 (79.8%) cases had non-small cell lung cancer (NSCLC), 14 (14.9%) had small cell lung cancer (SCLC), and 5 (5.3%) had lung neuroendocrine neoplasm (NEN). All LC types showed increased CXCR4 expression on PET (SUV_max) and FACS (MFI). However, both these parameters (mean SUV_max = 10.3 ± 5.0; mean MFI = 349.0 ± 99.0) were significantly (p = 0.005) higher in SCLC as compared to those in NSCLC and lung NEN. The mean SUV_max in adenocarcinoma (n = 16) was 8.0 ± 1.9 which was significantly (p = 0.003) higher than in squamous cell carcinoma (n = 54; 6.2 ± 2.1) and in not-otherwise specified (NOS) sub-types (n = 5; 5.8 ± 1.5) of NSCLC. A significant correlation (r = 0.697; p = 001) was seen between SUV_max and MFI values in squamous cell NSCLC as well as in NSCLC adenocarcinoma (r = 0.538, p = 0.031) which supports the specific in vivo uptake of [⁶⁸Ga]Ga-Pentixafor by CXCR4 receptors. However, this correlation was not significant in SCLC (r = 0.435, p = 0.121) and NEN (r = 0.747, p = 0.147) which may be due to the small sample size. [⁶⁸Ga]Ga-Pentixafor PET/CT provided good sensitivity (85.7%) and specificity (78.1%) for differentiating SCLC from NSCLC (ROC cutoff SUV_max = 7.2). This technique presented similar sensitivity (87.5%) and specificity (71.4%) (ROC cutoff SUV_max = 6.7) for differentiating adenocarcinoma and squamous cell variants of NSCLC.

CONCLUSION

The high sensitivity and specificity of [⁶⁸Ga]Ga-Pentixafor PET/CT for in vivo targeting of CXCR4 receptors in lung cancer can thus be used effectively for the response assessment and development of CXCR4-based radioligand therapies in LC.

Lung nodule pre-diagnosis and insertion path planning for chest CT images

Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture. The overall process included lung parenchyma segmentation, classification and pre-diagnosis, 3-D reconstruction and path planning, and experimental verification. First, accurate lung parenchyma segmentation in chest CT images was achieved using digital image processing technologies, such as adaptive gray threshold, connected area labeling, and mathematical morphological boundary repair. Multi-feature weight assignment was then adopted to establish a multi-level classification criterion to complete the classification and pre-diagnosis of pulmonary nodules. Next, 3-D reconstruction of lung regions was performed using voxelization, and on its basis, a feasible local optimal insertion path with an insertion point could be found by avoiding sternums and/or key tissues in terms of the needle-inserting path. Finally, CT images of 900 patients from Lung Image Database Consortium and Image Database Resource Initiative were chosen to verify the validity of pulmonary nodule diagnosis. Our previously designed surgical robotic system and a custom thoracic model were used to validate the effectiveness of the insertion path. This work can not only assist doctors in completing the pre-diagnosis of pulmonary nodules but also provide a reference for clinical biopsy puncture of suspected malignant nodules considered by doctors.

Investigation of small lung lesion detection for lung cancer screening in low dose FDG PET imaging by deep neural networks

Purpose

FDG PET imaging is often recommended for the diagnosis of pulmonary nodules after indeterminate low dose CT lung cancer screening. Lowering FDG injecting is desirable for PET imaging. In this work, we aimed to investigate the performance of a deep learning framework in the automatic diagnoses of pulmonary nodules at different count levels of PET imaging.

Materials and methods

Twenty patients with 18F-FDG-avid pulmonary nodules were included and divided into independent training (60%), validation (20%), and test (20%) subsets. We trained a convolutional neural network (ResNet-50) on original DICOM images and used ImageNet pre-trained weight to fine-tune the model. Simulated low-dose PET images at the 9 count levels (20 × 10⁶, 15 × 10⁶, 10 × 10⁶, 7.5 × 10⁶, 5 × 10⁶, 2 × 10⁶, 1 × 10⁶, 0.5 × 10⁶, and 0.25 × 10⁶ counts) were obtained by randomly discarding events in the PET list mode data for each subject. For the test dataset with 4 patients at the 9 count levels, 3,307 and 3,384 image patches were produced for lesion and background, respectively. The receiver-operator characteristic (ROC) curve of the proposed model under the different count levels with different lesion size groups were assessed and the areas under the ROC curve (AUC) were compared.

Results

The AUC values were >0.98 for all count levels except for 0.5 and 0.25 million true counts (0.975 (CL 95%, 0.953-0.992) and 0.963 (CL 95%, 0.941-0.982), respectively). The AUC values were 0.941(CL 95%, 0.923-0.956), 0.993(CL 95%, 0.990-0.996) and 0.998(CL 95%, 0.996-0.999) for different groups of lesion size with effective diameter (R) <10 mm, 10-20 mm, and >20 mm, respectively. The count limit for achieving high AUC (≥0.96) for lesions with size R < 10 mm and R > 10 mm were 2 million (equivalent to an effective dose of 0.08 mSv) and 0.25 million true counts (equivalent to an effective dose of 0.01 mSv), respectively.

Conclusion

All of the above results suggest that the proposed deep learning based method may detect small lesions <10 mm at an effective radiation dose <0.1 mSv.

Advances in knowledge

We investigated the advantages and limitations of a fully automated lung cancer detection method based on deep learning models for data with different lesion sizes and different count levels, and gave guidance for clinical application.

Positron emission tomography imaging of lung cancer: An overview of alternative positron emission tomography tracers beyond F18 fluorodeoxyglucose

Lung cancer has been the leading cause of cancer-related mortality in China in recent decades. Positron emission tomography-computer tomography (PET/CT) has been established in the diagnosis of lung cancer. ¹⁸F-FDG is the most widely used PET tracer in foci diagnosis, tumor staging, treatment planning, and prognosis assessment by monitoring abnormally exuberant glucose metabolism in tumors. However, with the increasing knowledge on tumor heterogeneity and biological characteristics in lung cancer, a variety of novel radiotracers beyond ¹⁸F-FDG for PET imaging have been developed. For example, PET tracers that target cellular proliferation, amino acid metabolism and transportation, tumor hypoxia, angiogenesis, pulmonary NETs and other targets, such as tyrosine kinases and cancer-associated fibroblasts, have been reported, evaluated in animal models or under clinical investigations in recent years and play increasing roles in lung cancer diagnosis. Thus, we perform a comprehensive literature review of the radiopharmaceuticals and recent progress in PET tracers for the study of lung cancer biological characteristics beyond glucose metabolism.

The prognosis of non-small cell lung cancer patients according to endobronchial metastatic lesion

To evaluate the prognosis of non-small cell lung cancer (NSCLC) patients according to endobronchial metastatic lesion (EML), especially those not identified on positron emission tomography or computed tomography. We evaluated progression-free survival (PFS) and overall survival (OS) according to the presence of EML in patients with NSCLC who were diagnosed at a tertiary hospital between January 2010 and December 2019. A total of 364 patients were enrolled in this study. EML was found in 69 (19.0%) patients with NSCLC. In the patients with EML versus the patients without EML, median PFS was 7.0 (3.5–13.5) and 9.5 (5.5–17.5) months (P = 0.011), and median OS was 12.0 (6.0–30.0) versus 20.0 (10.0–39.0) months (P = 0.016), respectively. Median PFS and OS rates were highest in epidermal growth factor receptor (EGFR) (+) and EML (−) patients and lowest in EGFR (−) and EML (+) patients (P < 0.001). By multivariate cox regression analysis, PFS in overall patients with NSCLC was significantly associated with EML, EGFR mutation, performance status, and pleural effusion. NSCLC patients with EML had worse prognoses of PFS and OS than patients without EML.

A Role of Non-FDG Tracers in Lung Cancer?

Since the introduction of PET/CT hybrid imaging about two decades ago the landscape of oncological imaging has fundamentally changed, opening a new era of molecular imaging with emphasis on functional characterization of biological processes such as metabolism, cellular proliferation, hypoxia, apoptosis, angiogenesis and immune response. The most commonly assessed functional hallmark of cancer is the increased metabolism in tumor cells due to well-known Warburg effect, because of which FDG has been the most employed radiotracer, the so-called pan-cancer agent, in oncological imaging. However, several limitations such as low specificity and low sensitivity for several histopathological forms of lung cancer as well as high background uptake in the normal tissue of FDG imaging lead to numerous serious pitfalls. This restricts its utilization and diagnostic value in lung cancer imaging, even though this is currently considered to be the method of choice in pulmonary cancer imaging. Accurate initial tumor staging and therapy response monitoring with respect to the TNM criteria plays a crucial role in therapy planning and management in patients with lung cancer. To this end, many efforts have been made for decades to develop novel PET radiopharmaceuticals with innovative approaches that go beyond the assessment of increased glycolytic activity alone. Radiopharmaceuticals targeting DNA synthesis, amino acid metabolism, angiogenesis, or hypoxia have been extensively studied, leading to the emergence of indications for specific clinical questions or as a complementary imaging tool alongside existing conventional or FDG imaging. Nevertheless, despite some initial encouraging results, these tracers couldn't gain a widespread use and acceptance in clinical routine. However, given its mechanism of action and some initial pilot studies regarding lung cancer imaging, FAPI has emerged as a very promising alternative tool that could provide superior or comparable diagnostic performance to FDG imaging in lung cancer entities. Thus, in this review article, we summarized the current PET radiopharmaceuticals, different imaging approaches and discussed the potential benefits and clinical applications of these agents in lung cancer imaging.

Expert consensus on oncological [18F]FDG total-body PET/CT imaging (version 1)

BACKGROUND

[¹⁸F]FDG imaging on total-body PET/CT (TB PET/CT) scanners, with improved sensitivity, offers new potentials for cancer diagnosis, staging, and radiation treatment planning. This consensus provides the protocols for clinical practices with a goal of paving the way for future studies with the total-body scanners in oncological [¹⁸F]FDG TB PET/CT imaging.

METHODS

The consensus was summarized based on the published guidelines and peer-reviewed articles of TB PET/CT in the literature, along with the opinions of the experts from major research institutions with a total of 40,000 cases performed on the TB PET/CT scanners.

RESULTS

This consensus describes the protocols for routine and dynamic [¹⁸F]FDG TB PET/CT scanning focusing on the reduction of imaging acquisition time and FDG injected activity, which may serve as a reference for research and clinic oncological PET/CT studies.

CONCLUSION

This expert consensus focuses on the reduction of acquisition time and FDG injected activity with a TB PET/CT scanner, which may improve the patient throughput or reduce the radiation exposure in daily clinical oncologic imaging.

KEY POINTS

• [¹⁸F]FDG-imaging protocols for oncological total-body PET/CT with reduced acquisition time or with different FDG activity levels have been summarized from multicenter studies. • Total-body PET/CT provides better image quality and improved diagnostic insights. • Clinical workflow and patient management have been improved.

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

PET-CT in Clinical Adult Oncology: II. Primary Thoracic and Breast Malignancies

Simple Summary

Positron emission tomography (PET), typically combined with computed tomography (CT), has become a critical advanced imaging technique in oncology. With PET-CT, a radioactive molecule (radiotracer) is injected in the bloodstream and localizes to sites of tumor because of specific cellular features of the tumor that accumulate the targeting radiotracer. The CT scan, performed at the same time, provides information to facilitate assessment of the amount of radioactivity from deep or dense structures, and to provide detailed anatomic information. PET-CT has a variety of applications in oncology, including staging, therapeutic response assessment, restaging, and surveillance. This series of six review articles provides an overview of the value, applications, and imaging and interpretive strategies of PET-CT in the more common adult malignancies. The second article in this series addresses the use of PET-CT in breast cancer and other primary thoracic malignancies.

Abstract

Positron emission tomography combined with x-ray computed tomography (PET-CT) is an advanced imaging modality with oncologic applications that include staging, therapy assessment, restaging, and surveillance. This six-part series of review articles provides practical information to providers and imaging professionals regarding the best use of PET-CT for the more common adult malignancies. The second article of this series addresses primary thoracic malignancy and breast cancer. For primary thoracic malignancy, the focus will be on lung cancer, malignant pleural mesothelioma, thymoma, and thymic carcinoma, with an emphasis on the use of FDG PET-CT. For breast cancer, the various histologic subtypes will be addressed, and will include ¹⁸F fluorodeoxyglucose (FDG), recently Food and Drug Administration (FDA)-approved ¹⁸F-fluoroestradiol (FES), and ¹⁸F sodium fluoride (NaF). The pitfalls and nuances of PET-CT in breast and primary thoracic malignancies and the imaging features that distinguish between subcategories of these tumors are addressed. This review will serve as a resource for the appropriate roles and limitations of PET-CT in the clinical management of patients with breast and primary thoracic malignancies for healthcare professionals caring for adult patients with these cancers. It also serves as a practical guide for imaging providers, including radiologists, nuclear medicine physicians, and their trainees.

Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection

Background

The management of a solitary pulmonary nodule is a challenging issue in pulmonary disease. Although many factors have been defined as predictors for malignancy in solitary pulmonary nodules, the accurate diagnosis can only be established with the permanent histological diagnosis.

Objective

We tried to clarify the possible predictors of malignancy in solitary pulmonary nodules in patients who had definitive histological diagnosis.

Methods

We made a retrospective study to collect the data of patients with solitary pulmonary nodules who had histological diagnosis either before or after surgery. We made a statistical analysis of both the clinic and radiological features of these nodules with respect to malignancy both in contingency tables and with logistic regression analysis.

Results

We had a total of 223 patients with a radiological diagnosis of solitary pulmonary nodule. Age, smoking status and pack years of smoking, maximum standardized uptake value (SUVmax), and radiological features such as solid component, spiculation, pleural tag, lobulation, calcification, and higher density were significant predictors of malignancy in contingency tables. Age, smoking status and smoking (pack/year), SUVmax, and radiological features including spiculation, pleural tag, lobulation, calcification, and higher density were the significant predictors in univariate analysis. However, multivariate analysis revealed only SUVmax greater than 2.5 (p < 0.0001), spiculation (p = 0.009), and age older than 61 years (p = 0.015) as the significant predictors for malignancy.

Conclusion

Age, SUVmax, and spiculation are the independent predictors of malignancy in patients with solitary pulmonary nodules.

The Need to Pair Molecular Monitoring Devices with Molecular Imaging to Personalize Health

By enabling the non-invasive monitoring and quantification of biomolecular processes, molecular imaging has dramatically improved our understanding of disease. In recent years, non-invasive access to the molecular drivers of health versus disease has emboldened the goal of precision health, which draws on concepts borrowed from process monitoring in engineering, wherein hundreds of sensors can be employed to develop a model which can be used to preventatively detect and diagnose problems. In translating this monitoring regime from inanimate machines to human beings, precision health posits that continual and on-the-spot monitoring are the next frontiers in molecular medicine. Early biomarker detection and clinical intervention improves individual outcomes and reduces the societal cost of treating chronic and late-stage diseases. However, in current clinical settings, methods of disease diagnoses and monitoring are typically intermittent, based on imprecise risk factors, or self-administered, making optimization of individual patient outcomes an ongoing challenge. Low-cost molecular monitoring devices capable of on-the-spot biomarker analysis at high frequencies, and even continuously, could alter this paradigm of therapy and disease prevention. When these devices are coupled with molecular imaging, they could work together to enable a complete picture of pathogenesis. To meet this need, an active area of research is the development of sensors capable of point-of-care diagnostic monitoring with an emphasis on clinical utility. However, a myriad of challenges must be met, foremost, an integration of the highly specialized molecular tools developed to understand and monitor the molecular causes of disease with clinically accessible techniques. Functioning on the principle of probe-analyte interactions yielding a transducible signal, probes enabling sensing and imaging significantly overlap in design considerations and targeting moieties, however differing in signal interpretation and readout. Integrating molecular sensors with molecular imaging can provide improved data on the personal biomarkers governing disease progression, furthering our understanding of pathogenesis, and providing a positive feedback loop toward identifying additional biomarkers and therapeutics. Coupling molecular imaging with molecular monitoring devices into the clinical paradigm is a key step toward achieving precision health.

A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging

Over the past few years, positron emission tomography/computed tomography (PET/CT) imaging for computer-aided diagnosis has received increasing attention. Supervised deep learning architectures are usually employed for the detection of abnormalities, with anatomical localization, especially in the case of CT scans. However, the main limitations of the supervised learning paradigm include (i) large amounts of data required for model training, and (ii) the assumption of fixed network weights upon training completion, implying that the performance of the model cannot be further improved after training. In order to overcome these limitations, we apply a few-shot learning (FSL) scheme. Contrary to traditional deep learning practices, in FSL the model is provided with less data during training. The model then utilizes end-user feedback after training to constantly improve its performance. We integrate FSL in a U-Net architecture for lung cancer lesion segmentation on PET/CT scans, allowing for dynamic model weight fine-tuning and resulting in an online supervised learning scheme. Constant online readjustments of the model weights according to the user's feedback, increase the detection and classification accuracy, especially in cases where low detection performance is encountered. Our proposed method is validated on the Lung-PET-CT-DX TCIA database. PET/CT scans from 87 patients were included in the dataset and were acquired 60 minutes after intravenous¹⁸F-FDG injection. Experimental results indicate the superiority of our approach compared to other state of the art methods.

The Evolving Landscape of Lung Cancer Surgical Resection: An Update for Radiologists With Focus on Key Chest CT Findings

Evolution of the multimodality management of early lung cancer, including progress in surgical techniques, has introduced the possibility of resection for lung cancer cases that historically were considered unresectable (e.g., select cases of T4 disease and oligometastatic disease). However, the TNM classification does not uniformly correlate with lung cancer operability and resectability. Radiologic evaluation is therefore critical in identifying patients' suitability to undergo lung cancer resection and in guiding the selection of a surgical approach from among a range of such approaches, including wedge resection, segmentectomy, lobectomy, bilobectomy, and pneumonectomy. The radiologist must understand the available surgical options, along with their advantages and disadvantages, and provide a report that includes critical information on tumor size, location, and extension and anatomic relations that may influence the surgical technique. Preoperative CT findings may also help predict expected postoperative lung function and the associated impact on the postoperative course of the patient. This article reviews the role of chest CT in the preoperative evaluation of lung cancer, focusing on the key CT findings that help direct surgical decision making in the context of an expanding range of patients who may be considered candidates for resection.

Solitary multicystic lesion lung cancer: two case reports and review of the literature

Background

Lung cancer associated with cystic airspaces, especially solitary multicystic lesion lung cancer, is a rare disease (a rare imaging performance of non-small cell lung cancer). It is difficult to diagnose owing to the lack of a clear definition; therefore, diagnosis of these neoplastic lesions remains challenging.

Case presentation

We outlined two cases of elderly Chinese men who were admitted to the hospital with a solitary multicystic lesion of the lung and subsequent surgical resection, confirming a diagnosis of adenocarcinoma.

Conclusions

For solitary pulmonary cystic airspaces (especially solitary multicystic lung lesions), it is important to properly recognise their imaging features. Due to the possibility of malignancies, timely surgery is an effective treatment strategy for early diagnosis.

Metabolic Phenotypes, Dependencies, and Adaptation in Lung Cancer

Lung cancer is a heterogeneous disease that is subdivided into histopathological subtypes with distinct behaviors. Each subtype is characterized by distinct features and molecular alterations that influence tumor metabolism. Alterations in tumor metabolism can be exploited by imaging modalities that use metabolite tracers for the detection and characterization of tumors. Microenvironmental factors, including nutrient and oxygen availability and the presence of stromal cells, are a critical influence on tumor metabolism. Recent technological advances facilitate the direct evaluation of metabolic alterations in patient tumors in this complex microenvironment. In addition, molecular alterations directly influence tumor cell metabolism and metabolic dependencies that influence response to therapy. Current therapeutic approaches to target tumor metabolism are currently being developed and translated into the clinic for patient therapy.

Multimodal Imaging-Based Potential Visualization of the Tumor Microenvironment in Bone Metastasis

Bone metastasis (BM) is the most common malignant bone tumor and a significant cause of morbidity and mortality for patients with cancer. Compared to other metastatic organs, bone has unique characteristics in terms of the tumor microenvironment (TME). Precise assessments of the TME in BM could be an important step for developing an optimized management plan for patient care. Imaging approaches for BM have several advantages, such as biopsy not being required, multiple site evaluation, and serial assessment in the same sites. Owing to the developments of new imaging tracers or imaging modalities, bone TME could be visualized using multimodal imaging techniques. In this review, we describe the BM pathophysiology, diagnostic principles of major imaging modalities, and clinically available imaging modalities to visualize the TME in BM. We also discuss how the interactions between various factors affecting the TME could be visualized using multimodal imaging techniques.

Prognostication Based on Texture Analysis of Baseline 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Nonsmall-Cell Lung Carcinoma Patients Who Underwent Platinum-Based Chemotherapy as First-Line Treatment

OBJECTIVE

Our study aims to establish the potential for tumor heterogeneity evaluated using 18F fluorodeoxyglucose positron emission tomography/computed tomography (F-18 FDG PET/CT) texture analysis in nonsmall-cell lung carcinoma (NSCLC) patients who underwent platinum-based chemotherapy to provide an independent marker for overall survival (OS) of more than 1-year.

MATERIALS AND METHODS

A total of 42 patients (34 male and 8 female) with biopsy-proven NSCLC and mean age 55.33 ± 10.71 years who underwent a baseline F-18 FDG PET/CT and received platinum-based chemotherapy as first-line treatment were retrospectively included in the study. Ten first order, 21 s order texture parameters and 7 SUV and metabolic tumor volume (MTV) based metabolic parameters were calculated. All these parameters were compared between the two survival groups based on OS ≥1 year and OS <1 year. Cut-offs of significant parameters were determined using receiver operating characteristic curve analysis. Survival patterns were compared by log-rank test and presented using Kaplan-Meier curves. Cox proportion hazard model was used to determine the independent prognostic marker for 1 year OS.

RESULTS

In univariate survival analysis, 3 first order texture parameters (i.e. mean, median, root mean square with hazard ratios [HRs] 2.509 [P = 0.034], 2.590 [P = 0.05], 2.509 [P = 0.034], respectively) and 6 s order texture parameters (i.e. mean, auto correlation, cluster prominence, cluster shade, sum average and sum variance with HRs 2.509 [P = 0.034], 2.509 [P = 0.034], 3.929 [0.007], 2.903 [0.018], 2.954 [0.016] and 2.906 [0.014], respectively) were significantly associated with 1 year OS in these patients. Among the metabolic parameters, only metabolic tumor volume whole-body was significantly associated with 1 year OS. In multivariate survival analysis, cluster prominence came out as the independent predictor of 1 year OS.

CONCLUSION

Texture analysis based on F-18 FDG PET/CT is potentially beneficial in the prediction of OS ≥1 year in NSCLC patients undergoing platinum-based chemotherapy as first-line treatment. Thus, can be used to stratify the patients which will not be benefitted with platinum-based chemotherapy and essentially needs to undergo some other therapy option.

Safety, tolerability and clinical implementation of 'ready-to-use' 68gallium-DOTA0-Tyr3-octreotide (68Ga-DOTATOC) (SomaKIT TOC) for injection in patients diagnosed with gastroenteropancreatic neuroendocrine tumours (GEP-NETs)

BACKGROUND

⁶⁸Ga-DOTA0-Tyr3-octreotide (⁶⁸Ga-DOTATOC) positron emission tomography-CT (PET-CT) has superior diagnostic performance compared to the licensed tracer OctreoScan single photon emission CT-CT in patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs). A new preparation of ⁶⁸Ga-DOTATOC using a new 'ready-to-use' ⁶⁸Ga-DOTATOC formulation for injection has been developed (⁶⁸Ga-DOTATOC (SomaKIT TOC)).

OBJECTIVES

This study aimed to assess the safety and tolerability of ⁶⁸Ga-DOTATOC (SomaKIT TOC) and evaluate the feasibility and robustness of implementing it in a NET clinical imaging service.

METHODS

A first-in-human phase I/II multicentre, open-label study of a single dose of ⁶⁸Ga-DOTATOC (SomaKIT TOC) 2 MBq/kg±10% (range 100-200 MBq) in patients with biopsy-proven grade 1-2 GEP-NETs. PET-CT was performed post injection. Patients were followed up for 28 days. We next implemented this new synthesis methodology in a clinical service assessed over 11 months.

RESULTS

Twenty consenting patients were recruited; 14 males, 6 females; mean (SD) age 58 years (12); NET grade 1 (70%), grade 2 (30%); and 75% with stage IV disease. Twelve patients experienced at least one adverse event (AE) during the study with no grade 3-4 toxicities. Only four AEs were classified as possibly (headache (n=1; 4%), nausea (1; 4%)) or probably (dysgeusia (1; 4%), paraesthesia (1; 4%)) related to the study preparation. One hundred thirteen vials of ⁶⁸Ga-DOTATOC (SomaKIT TOC) were synthesised with the 'kit' over a period of 11 months for clinical utility. Only 2/113 vials (1.77%) were rejected.

CONCLUSIONS

The new ready-to-use preparation of ⁶⁸Ga-DOTATOC (SomaKIT TOC) for injection was safe and well tolerated. This has led to the world's first (EMA) licensed ⁶⁸Ga-DOTATOC (SomaKIT TOC) radiopharmaceutical for the utility of PET imaging in patients with NETs. This preparation can be robustly implemented into routine clinical practice.

A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans

Simple Summary

The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to evaluate the discriminability of a new radiomic feature, nodule edge/interface sharpness (NIS), for distinguishing lung adenocarcinomas from benign granulomas on non-contrast CT scans. Moreover, we aim to evaluate whether NIS can improve the performance of Lung-RADS, by reclassifying benign nodules that were initially assessed as suspicious. In a cohort of 352 patients with diagnostic non-contrast CT scans, NIS radiomics was able to classify nodules with an area under the receiver operating characteristic curve (ROC AUC) of 0.77, and when combined with intra-tumoral textural and shape features, classification performance increased to AUC of 0.84. Additionally, the NIS classifier correctly reclassified 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS. Combining NIS with Lung-RADS has the potential to alter patient management by significantly decreasing unnecessary biopsies/follow up imaging.

Abstract

The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (S_t, N = 145), validation (S_v, N = 145), and independent validation (S_iv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on S_t, S_v, and S_iv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in S_v that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on S_v.

Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

BACKGROUND

The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (¹⁸F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT).

PATIENTS AND METHODS

Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic ¹⁸F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUV_max) was measured on conventional ¹⁸F-FDG PET/CT images. The influx constant (K_i ) was calculated from the dynamic ¹⁸F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test.

RESULTS

Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUV_max values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average K_i values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min^-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613).

CONCLUSIONS

Dynamic ¹⁸F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.

Malignancy Prediction Capacity and Possible Prediction Model of Circulating Tumor Cells for Suspicious Pulmonary Lesions

More and more undetermined lung lesions are being identified in routine lung cancer screening. The aim of this study was to try to establish a malignancy prediction model according to the tumor presentations. From January 2017 to December 2018, 50 consecutive patients who were identified with suspicious lung lesions were enrolled into this study. Medical records were reviewed and tumor macroscopic and microscopic presentations were collected for analysis. Circulating tumor cells (CTC) were found to differ between benign and malignant lesions (p = 0.03) and also constituted the highest area under the receiver operation curve other than tumor presentations (p = 0.001). Since tumor size showed the highest sensitivity and CTC revealed the best specificity, a malignancy prediction model was proposed. Akaike information criterion (A.I.C.) of the combined malignancy prediction model was 26.73, which was lower than for tumor size or CTCs alone. Logistic regression revealed that the combined malignancy prediction model showed marginal statistical trends (p = 0.0518). In addition, the 95% confidence interval of combined malignancy prediction model showed less wide range than tumor size ≥ 0.7 cm alone. The calculated probability of malignancy in patients with tumor size ≥ 0.7 cm and CTC > 3 was 97.9%. By contrast, the probability of malignancy in patients whose tumor size was < 0.7 cm, and CTC ≤ 3 was 22.5%. A combined malignancy prediction model involving tumor size followed by the CTC count may provide additional information to assist decision making. For patients who present with tumor size ≥ 0.7 cm and CTC counts > 3, aggressive management should be considered, since the calculated probability of malignancy was 97.9%.

Evolving target volume concepts in locally advanced non-small cell lung cancer

Radiotherapy (RT) target volume concepts for locally advanced lung cancer have been under discussion for years. Although they may be as important as treatment doses, many aspects of them are still based on conventions, which, due to the paucity of prospective data, rely on long-term practice or on clinical knowledge and experience (e.g., on patterns of spread or recurrence). However, in recent years, large improvements have been made in medical imaging and molecular imaging methods have been implemented, which are of great interest in RT. For lung cancer, in recent years, 18F-fluoro-desoxy-glucose (FDG)-positron-emission tomography (PET)/computed tomography (CT) has shown a superior diagnostic accuracy as compare to conventional imaging and has become an indispensable standard tool for diagnostic workup, staging and response assessment. This offers the chance to optimize target volume concepts in relation to modern imaging. While actual recommendations as the EORTC or ESTRO-ACROP guidelines already include imaging standards, the recently published PET-Plan trial prospectively investigated conventional versus imaging guided target volumes in relation to patient outcome. The results of this trial may help to further refine standards. The current review gives a practical overview on procedures for pre-treatment imaging and target volume delineation in locally advanced non-small cell lung cancer (NSCLC) in synopsis of the procedures established by the PET-Plan trial with the actual EORTC and ACROP guidelines.

Advances in the In Vivo Molecular Imaging of Invasive Aspergillosis

Invasive pulmonary aspergillosis (IPA) is a life-threatening infection of immunocompromised patients with Aspergillus fumigatus, a ubiquitous environmental mould. While there are numerous functioning antifungal therapies, their high cost, substantial side effects and fear of overt resistance development preclude permanent prophylactic medication of risk-patients. Hence, a fast and definitive diagnosis of IPA is desirable, to quickly identify those patients that really require aggressive antimycotic treatment and to follow the course of the therapeutic intervention. However, despite decades of research into this issue, such a diagnostic procedure is still not available. Here, we discuss the array of currently available methods for IPA detection and their limits. We then show that molecular imaging using positron emission tomography (PET) combined with morphological computed tomography or magnetic imaging is highly promising to become a future non-invasive approach for IPA diagnosis and therapy monitoring, albeit still requiring thorough validation and relying on further acceptance and dissemination of the approach. Thereby, our approach using the A. fumigatus-specific humanized monoclonal antibody hJF5 labelled with ⁶⁴Cu as PET-tracer has proven highly effective in pre-clinical models and hence bears high potential for human application.

Heterogeneity of Glucose Transport in Lung Cancer

Increased glucose uptake is a known hallmark of cancer. Cancer cells need glucose for energy production via glycolysis and the tricarboxylic acid cycle, and also to fuel the pentose phosphate pathway, the serine biosynthetic pathway, lipogenesis, and the hexosamine pathway. For this reason, glucose transport inhibition is an emerging new treatment for different malignancies, including lung cancer. However, studies both in animal models and in humans have shown high levels of heterogeneity in the utilization of glucose and other metabolites in cancer, unveiling a complexity that is difficult to target therapeutically. Here, we present an overview of different levels of heterogeneity in glucose uptake and utilization in lung cancer, with diagnostic and therapeutic implications.

Clinicopathological characteristics of solitary cavitary lung cancer: a case-control study

Background

With the emerging radiological techniques and the increasing incidence of adenocarcinoma, the composition and structure of cavitary lung cancer have been significantly changed. The aim of the study was to demonstrate clinicopathological characteristics of solitary cavitary lung cancer which was ≤3 cm.

Methods

A case-control study was designed through retrospective data analysis of clinicopathological data of 946 cases with solitary lung cancer smaller than 3 cm. Univariable and multivariable analysis were used to identify the risk factors of cavitation.

Results

Cavitary lung cancer occurred more frequently in patients who were elderly (P=0.044), male (P=0.004), who had a smoking history (P=0.018), higher carcinoembryonic antigen (CEA) level (P<0.001), peripheral lesions (P=0.017), solid nodules (P<0.001), spiculation (P=0.003), vascular convergence (P<0.001), air bronchogram (P=0.004), larger tumor size (P<0.001), advanced T stage (P<0.001), lymph node metastasis (P=0.028) and advanced pTNM stage (P=0.004). In addition, cavitary lung cancer was more common in papillary predominant tumors (P=0.017), while noncavitary lung cancer occurred more frequently in AIS/MIA (P=0.002) and lepidic predominant tumors (P<0.001). It was confirmed that cavitation was significantly associated with elderly (P=0.013), male (P=0.003), larger maximum tumor diameter (P<0.001), solid nodules (P<0.001), larger pT size (P=0.016) and advanced pN stage (P=0.036) in multivariable analysis. ROC curves showed that the AUV was greater in maximum tumor diameter than in pT size predicting cavitation (0.71 vs. 0.66). A cut off value of 20.9 mm showed a discriminatory power of cavitation with a sensitivity of 68.7% and a specificity of 71.2%.

Conclusions

Comparing with noncavitary lung cancer, cavitary lung cancer smaller than 3 cm may have worse prognostic clinical, radiological and pathological characteristics. Especially, cavitary lung cancer present as more solid nodules on CT images and present with more invasive on pathological findings.

Mid-position treatment strategy for locally advanced lung cancer: a dosimetric study

OBJECTIVE

The internal target volume (ITV) strategy generates larger planning target volumes (PTVs) in locally advanced non-small cell lung cancer (LA-NSCLC) than the Mid-position (Mid-p) strategy. We investigated the benefit of the Mid-p strategy regarding PTV reduction and dose to the organs at risk (OARs).

METHODS

44 patients with LA-NSCLC were included in a randomized clinical study to compare ITV and Mid-p strategies. GTV were delineated by a physician on maximum intensity projection images and on Mid-p images from four-dimensional CTs. CTVs were obtained by adding 6 mm uniform margin for microscopic extension. CTV to PTV margins were calculated using the van Herk's recipe for setup and delineation errors. For the Mid-p strategy, the mean target motion amplitude was added as a random error. For both strategies, three-dimensional conformal plans delivering 60-66 Gy to PTV were performed. PTVs, dose-volume parameters for OARs (lung, esophagus, heart, spinal cord) were reported and compared.

RESULTS

With the Mid-p strategy, the median of volume reduction was 23.5 cm3 (p = 0.012) and 8.8 cm3 (p = 0.0083) for PTVT and PTVN respectively; the median mean lung dose reduction was 0.51 Gy (p = 0.0057). For 37.1% of the patients, delineation errors led to smaller PTV with the ITV strategy than with the Mid-p strategy.

CONCLUSION

PTV and mean lung dose were significantly reduced using the Mid-p strategy. Delineation uncertainty can unfavorably impact the advantage.

ADVANCES IN KNOWLEDGE

To the best of our knowledge, this is the first dosimetric comparison study between ITV and Mid-p strategies for LA-NSCLC.

Advancing Aptamers as Molecular Probes for Cancer Theranostic Applications-The Role of Molecular Dynamics Simulation

Theranostics cover emerging technologies for cell biomarking for disease diagnosis and targeted introduction of drug ingredients to specific malignant sites. Theranostics development has become a significant biomedical research endeavor for effective diagnosis and treatment of diseases, especially cancer. An efficient biomarking and targeted delivery strategy for theranostic applications requires effective molecular coupling of binding ligands with high affinities to specific receptors on the cancer cell surface. Bioaffinity offers a unique mechanism to bind specific target and receptor molecules from a range of non-targets. The binding efficacy depends on the specificity of the affinity ligand toward the target molecule even at low concentrations. Aptamers are fragments of genetic materials, peptides, or oligonucleotides which possess enhanced specificity in targeting desired cell surface receptor molecules. Aptamer-target binding results from several inter-molecular interactions including hydrogen bond formation, aromatic stacking of flat moieties, hydrophobic interaction, electrostatic, and van der Waals interactions. Advancements in Systematic Evolution of Ligands by Exponential Enrichment (SELEX) assay has created the opportunity to artificially generate aptamers that specifically bind to desired cancer and tumor surface receptors with high affinities. This article discusses the potential application of molecular dynamics (MD) simulation to advance aptamer-mediated receptor targeting in targeted cancer therapy. MD simulation offers real-time analysis of the molecular drivers of the aptamer-receptor binding and generate optimal receptor binding conditions for theranostic applications. The article also provides an overview of different cancer types with focus on receptor biomarking and targeted treatment approaches, conventional molecular probes, and aptamers that have been explored for cancer cells targeting.