Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0

Prognostic significance of pretreatment PET parameters in inoperable, node-positive NSCLC patients with poor prognostic factors undergoing hypofractionated radiotherapy: a single-institution retrospective study

BACKGROUND

Node-positive non-small cell lung cancers (NSCLCs) present a challenge for treatment decisions, particularly in patients ineligible for concurrent chemoradiotherapy (CRT) due to poor performance status and compromised lung function. We aimed to investigate the prognostic value of pretreatment positron emission tomography (PET) parameters in high-risk patients undergoing hypofractionated radiotherapy.

METHODS

A retrospective analysis was conducted on 42 consecutive patients with inoperable node-positive NSCLC, who underwent hypofractionated radiotherapy between 2014 and 2021 at a single institution. Clinical, treatment-related, and [18F]FDG PET-based parameters were correlated with progression-free survival (PFS) and overall survival (OS). Median dichotomisation was performed to establish risk groups. Statistical analyses included univariable and multivariable Cox regression and Kaplan-Meier survival analyses.

RESULTS

After a median follow-up of 47.1 months (range: 0.5-101.7), the median PFS and OS were 11.5 months (95% CI: 7.4-22.0), and 24.3 months (95% CI: 14.1-31.8). In univariable Cox regression analysis, significant predictors of PFS included receipt of salvage systemic treatment (p=0.007), SUVmax (p=0.032), and tMTV (p=0.038). Similarly, ECOG-PS (p=0.014), Histology (p=0.046), and tMTV (p=0.028) were significant predictors of OS. Multivariable Cox regression analysis (MVA) identified SUVmax as a significant predictor for PFS [HR: 2.29 (95% CI: 1.02-5.15); p=0.044]. For OS, ECOG-PS remained a significant prognosticator [HR: 3.53 (95% CI: 1.49-8.39); p=0.004], and tMTV approached significance [HR: 2.24 (95% CI: 0.95-5.26); p=0.065]. Furthermore, the high tMTV group exhibited a median PFS of 5.3 months [95% CI: 2.8-10.4], while the low tMTV group had a PFS of 15.2 months [95% CI: 10.1-33.5] (p=0.038, log-rank test). Median OS was 33.5 months [95% CI: 18.3-56.8] for tMTV ≤ 36.6 ml vs. 14.1 months [95% CI: 8.1-27.2] for tMTV > 36.6 ml (p=0.028, log-rank test).

CONCLUSION

Pretreatment PET parameters, especially tMTV, hold promise as prognostic indicators in NSCLC patients undergoing hypofractionated radiotherapy. The study highlights the potential of PET metrics as biomarkers for patient stratification.

EDB-FN-targeted probes for near infrared fluorescent imaging and positron emission tomography imaging of breast cancer in mice

The extra domain B splice variant of fibronectin (EDB-FN), which is overexpressed in several cancers, is an approved diagnostic and therapeutic target of cancers. The aim of this study was to evaluate the EDB-FN-targeting peptide EDBp as a noninvasive imaging modality for molecular imaging of breast cancer in mice. Western blot, flow cytometry and immunofluorescence were used to assess the expression level of EDB-FN and its binding to EDRp in MCF7, SKBR3, 4T1, EMT6, MDA-MB-231 and MDA-MB-453 cells. Establishment MDA-MB-231-luc cells-based subcutaneous tumor model mice or pulmonary metastasis model mice. The EDRp molecular probes to perform fluorescent probes for near-infrared fluorescence (NIRF)·and PET imaging of model mice. Our results demonstrate that EDBp-Cy5 had a strong binding ability to the MDA-MB-231 cells and exhibited specific tumor accumulation in MDA-MB-231 subcutaneous and pulmonary metastasis model mice. Importantly, the EDBp peptide-based radiotracer [18F]-AlF-NOTA-EDBp provided excellent diagnostic value for positron emission tomography (PET) imaging of breast cancer, especially in subcutaneous model mice. The uptake of [18F]-AlF-NOTA-EDBp in subcutaneous tumors (6.53 ± 0.89%, ID/g) was unexpectedly higher than that in the kidney (4.96 ± 0.20, %ID/g). The high tumor uptake of these probes in mice suggests their potential for application in imaging of EDB-FN-positive breast cancer for disease staging of regional and distant metastases.

Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs

This work presents the application of an Encoder-Decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real-world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks. We demonstrate a high-quality automated segmentation prediction of thoracic CT scans that correctly delineates the infected regions of the lungs. This segmentation automation can be used as a tool to speed up the contouring process, either to check manual contouring in place of a peer checking, when not possible or to give a rapid indication of infection to be referred for further treatment, thus saving time and resources. In contrast, manual contouring is a time-consuming process in which a professional would contour each patient one by one to be later checked by another professional. The proposed model uses approximately 49 k parameters while others average over 1,000 times more parameters. As our approach relies on a very compact model, shorter training times are observed, which make it possible to easily retrain the model using other data and potentially afford "personalised medicine" workflows. The model achieves similarity scores of Specificity (Sp) = 0.996 ± 0.001, Accuracy (Acc) = 0.994 ± 0.002 and Mean absolute error (MAE) = 0.0075 ± 0.0005.

Guardians of precision: advancing radiation protection, safety, and quality systems in nuclear medicine

BACKGROUND

In the rapidly evolving field of nuclear medicine, the paramount importance of radiation protection, safety, and quality systems cannot be overstated. This document provides a comprehensive analysis of the intricate regulatory frameworks and guidelines, meticulously crafted and updated by national and international regulatory bodies to ensure the utmost safety and efficiency in the practice of nuclear medicine.

METHODS

We explore the dynamic nature of these regulations, emphasizing their adaptability in accommodating technological advancements and the integration of nuclear medicine with other medical and scientific disciplines.

RESULTS

Audits, both internal and external, are spotlighted for their pivotal role in assessing and ensuring compliance with established standards, promoting a culture of continuous improvement and excellence. We delve into the significant contributions of entities like the International Atomic Energy Agency (IAEA) and relevant professional societies in offering universally applicable guidelines that amalgamate the latest in scientific research, ethical considerations, and practical applicability.

CONCLUSIONS

The document underscores the essence of international collaborations in pooling expertise, resources, and insights, fostering a global community of practice where knowledge and innovations are shared. Readers will gain an in-depth understanding of the practical applications, challenges, and opportunities presented by these regulatory frameworks and audit processes. The ultimate goal is to inspire and inform ongoing efforts to enhance safety, quality, and effectiveness in nuclear medicine globally.

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.

68Ga-FAPI-04 PET/CT in Non-Small Cell Lung Cancer: Accurate Evaluation of Lymph Node Metastasis and Correlation with Fibroblast Activation Protein Expression

Fibroblast activation protein (FAP) is a promising diagnostic and therapeutic target in various solid tumors. This study aimed to assess the diagnostic efficiency of 68Ga-labeled FAP inhibitor (FAPI)-04 PET/CT for detecting lymph node metastasis in non-small cell lung cancer (NSCLC) and to investigate the correlation between tumor 68Ga-FAPI-04 uptake and FAP expression. Methods: We retrospectively enrolled 136 participants with suspected or biopsy-confirmed NSCLC who underwent 68Ga-FAPI-04 PET/CT for initial staging. The diagnostic performance of 68Ga-FAPI-04 for the detection of NSCLC was evaluated. The final histopathology or typical imaging features were used as the reference standard. The SUVmax and SUVmean, 68Ga-FAPI-avid tumor volume (FTV), and total lesion FAP expression (TLF) were measured and calculated. FAP immunostaining of tissue specimens was performed. The correlation between 68Ga-FAPI-04 uptake and FAP expression was assessed using the Spearman correlation coefficient. Results: Ninety-one participants (median age, 65 y [interquartile range, 58-70 y]; 69 men) with NSCLC were finally analyzed. In lesion-based analysis, the diagnostic sensitivity and positive predictive value of 68Ga-FAPI-04 PET/CT for detection of the primary tumor were 96.70% (88/91) and 100% (88/88), respectively. In station-based analysis, the diagnostic sensitivity, specificity, and accuracy for the detection of lymph node metastasis were 72.00% (18/25), 93.10% (108/116), and 89.36% (126/141), respectively. Tumor 68Ga-FAPI-04 uptake (SUVmax, SUVmean, FTV, and TLF) correlated positively with FAP expression (r = 0.470, 0.477, 0.582, and 0.608, respectively; all P ≤ 0.001). The volume parameters FTV and TLF correlated strongly with FAP expression in 31 surgical specimens (r = 0.700 and 0.770, respectively; both P < 0.001). Conclusion: 68Ga-FAPI-04 PET/CT had excellent diagnostic efficiency for detecting lymph node metastasis, and 68Ga-FAPI-04 uptake showed a close association with FAP expression in participants with NSCLC.

An 18F-FDG PET/CT and Mean Lung Dose Model to Predict Early Radiation Pneumonitis in Stage III Non-Small Cell Lung Cancer Patients Treated with Chemoradiation and Immunotherapy

Radiation pneumonitis (RP) that develops early (i.e., within 3 mo) (RPEarly) after completion of concurrent chemoradiation (cCRT) leads to treatment discontinuation and poorer survival for patients with stage III non-small cell lung cancer. Since no RPEarly risk model exists, we explored whether published RP models and pretreatment 18F-FDG PET/CT-derived features predict RPEarly Methods: One hundred sixty patients with stage III non-small cell lung cancer treated with cCRT and consolidative immunotherapy were analyzed for RPEarly Three published RP models that included the mean lung dose (MLD) and patient characteristics were examined. Pretreatment 18F-FDG PET/CT normal-lung SUV featured included the following: 10th percentile of SUV (SUVP10), 90th percentile of SUV (SUVP90), SUVmax, SUVmean, minimum SUV, and SD. Associations between models/features and RPEarly were assessed using area under the receiver-operating characteristic curve (AUC), P values, and the Hosmer-Lemeshow test (pHL). The cohort was randomly split, with similar RPEarly rates, into a 70%/30% derivation/internal validation subset. Results: Twenty (13%) patients developed RPEarly Predictors for RPEarly were MLD alone (AUC, 0.72; P = 0.02; pHL, 0.87), SUVP10, SUVP90, and SUVmean (AUC, 0.70-0.74; P = 0.003-0.006; pHL, 0.67-0.70). The combined MLD and SUVP90 model generalized in the validation subset and was deemed the final RPEarly model (RPEarly risk = 1/[1+e(- x )]; x = -6.08 + [0.17 × MLD] + [1.63 × SUVP90]). The final model refitted in the 160 patients indicated improvement over the published MLD-alone model (AUC, 0.77 vs. 0.72; P = 0.0001 vs. 0.02; pHL, 0.65 vs. 0.87). Conclusion: Patients at risk for RPEarly can be detected with high certainty by combining the normal lung's MLD and pretreatment 18F-FDG PET/CT SUVP90 This refined model can be used to identify patients at an elevated risk for premature immunotherapy discontinuation due to RPEarly and could allow for interventions to improve treatment outcomes.

Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy

PURPOSE

To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters.

METHODS

We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence.

RESULTS

In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69).

CONCLUSION

Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.

Clinical and pathologic staging accuracy in patients with synchronous multiple primary lung cancers

Background

The incidence of synchronous multiple primary lung cancer (SMPLC) is increasing, occurring in up to 20% of lung cancer patients. Accurately identifying SMPLC can be challenging, and failure to recognize SMPLC results in poor outcomes. We sought to assess the staging accuracy of patients with SMPLC at our tertiary institution.

Methods

We retrospectively reviewed all patients who were evaluated for lung cancer resection between January 2018 to September 2019. Patients with SMPLC were identified using the modified Martini-Melamed criteria. Preoperative imaging, clinical assessment, and pathologic interpretation were reviewed and compared to the final staging assigned by a multidisciplinary lung cancer tumor board to determine accuracy.

Results

Out of 227 patients presenting for lung cancer resection, 47 patients with 119 SMPLC were identified, of which 38 (80.9%) were incorrectly staged by at least one report. Incorrect staging was most common by computed tomography (CT) reports (n=33/47, 70.2%), followed by positron emission tomography-CT (PET-CT) reports (n=28/45, 62.2%), surgeons' clinical assessment (n=10/47, 21.3%), and histopathology reports (n=8/47, 17.0%). CT reports, when incorrect, under-staged 97.0% (n=32) of patients. PET-CT reports, when incorrect, over-staged 25.0% (n=7) of patients by reporting the second primary nodule to be "consistent with metastasis". Histopathology reports, when incorrect, over-staged 87.5% (n=7) of patients despite lack of lymph node involvement.

Conclusions

Patients with SMPLC are at risk of receiving incorrect treatment based on radiographic and histopathologic staging reports alone. The observed staging inaccuracies are concerning, necessitating increased awareness among physicians caring for lung cancer patients.

Radio-Guided Lung Surgery: A Feasible Approach for a Cancer Precision Medicine

BACKGROUND

Radio-guided surgery is a reliable approach used for localizing ground-glass opacities, lung nodules, and metastatic lymph nodes. Lung nodules, lymph node metastatic involvement, and ground-glass opacities often represent a challenge for surgical management and clinical work-up.

METHODS

PubMed research was conducted from January 1997 to June 2023 using the keywords "radioguided surgery and lung cancer".

RESULTS

Different studies were conducted with different tracers: technetium-99m-albumin macroaggregates, cyanoacrylate combined to technetium-99m-sulfur colloid, indium-111-pentetreotide, and fluorine-18-deoxyglucose. A study proposed naphthalocyanine radio-labeled with copper-64. Radio-guided surgery has been demonstrated to be a reliable approach in localizing a lesion, and has a low radiological burden for personnel exposure and low morbidity. The lack of necessity to conduct radio-guided surgery under fluoroscopy or echography makes this radio-guided surgery an easy way of performing precise surgical procedures.

CONCLUSIONS

Radio-guided surgery is a feasible approach useful for the intraoperative localization of ground-glass opacities, lung nodules, and metastatic lymph nodes. It is a valid alternative to the existing approaches due to its low cost, associated low morbidity, the possibility to perform the procedure after several hours, the low radiation dose applied, and the small amount of time that is required to perform it.

A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT

Introduction

Accurate delineation of tumor targets is crucial for stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC). This study aims to develop a deep learning-based segmentation approach to accurately and efficiently delineate NSCLC targets using diagnostic PET-CT and SBRT planning CT (pCT).

Methods

The diagnostic PET was registered to pCT using the transform matrix from registering diagnostic CT to the pCT. We proposed a 3D-UNet-based segmentation method to segment NSCLC tumor targets on dual-modality PET-pCT images. This network contained squeeze-and-excitation and Residual blocks in each convolutional block to perform dynamic channel-wise feature recalibration. Furthermore, up-sampling paths were added to supplement low-resolution features to the model and also to compute the overall loss function. The dice similarity coefficient (DSC), precision, recall, and the average symmetric surface distances were used to assess the performance of the proposed approach on 86 pairs of diagnostic PET and pCT images. The proposed model using dual-modality images was compared with both conventional 3D-UNet architecture and single-modality image input.

Results

The average DSC of the proposed model with both PET and pCT images was 0.844, compared to 0.795 and 0.827, when using 3D-UNet and nnUnet. It also outperformed using either pCT or PET alone with the same network, which had DSC of 0.823 and 0.732, respectively.

Discussion

Therefore, our proposed segmentation approach is able to outperform the current 3D-UNet network with diagnostic PET and pCT images. The integration of two image modalities helps improve segmentation accuracy.

Pretreatment 18-FDG-PET/CT parameters can serve as prognostic imaging biomarkers in recurrent NSCLC patients treated with reirradiation-chemoimmunotherapy

BACKGROUND AND PURPOSE

Our study aimed to assess whether quantitative pretreatment 18F-FDG-PET/CT parameters could predict prognostic clinical outcome of recurrent NSCLC patients who may benefit from ablative reirradiation.

MATERIALS AND METHODS

Forty-eight patients with recurrent NSCLC of all UICC stages who underwent ablative thoracic reirradiation were analyzed. Twenty-nine (60%) patients received immunotherapy with or without chemotherapy in addition to reirradiation. Twelve patients (25%) received reirradiation only and seven (15%) received chemotherapy and reirradiation. Pretreatment 18-FDG-PET/CT was mandatory in initial diagnosis and recurrence, based on which volumetric and intensity quantitative parameters were measured before reirradiation and their impact on overall survival, progression-free survival, and locoregional control was assessed.

RESULTS

With a median follow-up time of 16.7 months, the median OS was 21.8 months (95%-CI: 16.2-27.3). On multivariate analysis, OS and PFS were significantly influenced by MTV (p < 0.001 for OS; p = 0.006 for PFS), TLG (p < 0.001 for OS; p = 0.001 for PFS) and SUL peak (p = 0.0024 for OS; p = 0.02 for PFS) of the tumor and MTV (p = 0.004 for OS; p < 0.001 for PFS) as well as TLG (p = 0.007 for OS; p = 0.015 for PFS) of the metastatic lymph nodes. SUL peak of the tumor (p = 0.05) and the MTV of the lymph nodes (p = 0.003) were only PET quantitative parameters that significantly impacted LRC.

CONCLUSION

Pretreatment tumor and metastastic lymph node MTV, TLG and tumor SUL peak significantly correlated with clinical outcome in recurrent NSCLC patients treated with reirradiation-chemoimmunotherapy.

Metabolic patterns on [18F]FDG PET/CT in patients with unresectable stage III NSCLC undergoing chemoradiotherapy ± durvalumab maintenance treatment

PURPOSE

In patients with unresectable stage III non-small-cell lung cancer (NSCLC), durvalumab maintenance treatment after chemoradiotherapy (CRT) significantly improves survival. So far, however, metabolic changes of tumoral lesions and secondary lymphoid organs under durvalumab are unknown. Hence, we assessed changes on [18F]FDG PET/CT in comparison to patients undergoing CRT alone.

METHODS

Forty-three patients with [18F]FDG PET/CT both before and after standard CRT for unresectable stage III NSCLC were included, in 16/43 patients durvalumab maintenance treatment was initiated (CRT-IO) prior to the second PET/CT. Uptake of tumor sites and secondary lymphoid organs was compared between CRT and CRT-IO. Also, readers were blinded for durvalumab administration and reviewed scans for findings suspicious for immunotherapy-related adverse events (irAE).

RESULTS

Initial uptake characteristics were comparable. However, under durvalumab, diverging metabolic patterns were noted: There was a significantly higher reduction of tumoral uptake intensity in CRT-IO compared to CRT, e.g. median decrease of SUVmax -70.0% vs. -24.8%, p = 0.009. In contrast, the spleen uptake increased in CRT-IO while it dropped in CRT (median + 12.5% vs. -4.4%, p = 0.029). Overall survival was significantly longer in CRT-IO compared to CRT with few events (progression/death) noted in CRT-IO. Findings suggestive of irAE were present on PET/CT more often in CRT-IO (12/16) compared to CRT (8/27 patients), p = 0.005.

CONCLUSION

Durvalumab maintenance treatment after CRT leads to diverging tumoral metabolic changes, but also increases splenic metabolism and leads to a higher proportion of findings suggestive of irAE compared to patients without durvalumab. Due to significantly prolonged survival with durvalumab, survival analysis will be substantiated in correlation to metabolic changes as soon as more clinical events are present.

The Concept of Strength Through Synergy Applied to the Search of Powerful Prognostic Biomarkers in Gastroesophageal Cancer: An Example Based on Combining Clinicopathological Parameters, Imaging-Derived Sarcopenia Measurements, and Radiomic Features

ABSTRACT

Integrating clinical and pathological data together with imaging-derived information, such as radiomics and sarcopenia status, creating new combined biomarkers that increase the prognostic value compared with each of them used independently. The concept of strength through synergy, applicable in so many areas of life, is also demonstrated in this area of science and opens up innumerable pathways for improving patient care in cancer. This is as an example on how we can explore and make the most of all the information we already have (clinical, pathological, imaging), without the need for new invasive tests.

Changing Role of PET/CT in Cancer Care With a Focus on Radiotherapy

Positron emission tomography (PET) integrated with computed tomography (CT) has brought revolutionary changes in improving cancer care (CC) for patients. These include improved detection of previously unrecognizable disease, ability to identify oligometastatic status enabling more aggressive treatment strategies when the disease burden is lower, its use in better defining treatment targets in radiotherapy (RT), ability to monitor treatment responses early and thus improve the ability for early interventions of non-responding tumors, and as a prognosticating tool as well as outcome predicting tool. PET/CT has enabled the emergence of new concepts such as radiobiotherapy (RBT), radioimmunotherapy, theranostics, and pharmaco-radiotherapy. This is a rapidly evolving field, and this primer is to help summarize the current status and to give an impetus to developing new ideas, clinical trials, and CC outcome improvements.

FDG PET/CT for Primary Staging of Lung Cancer and Mesothelioma

Lung cancer is the leading cause of cancer-related mortality in the United States. Accurate staging at initial diagnosis determines appropriate treatment and is the most important predictor of survival. Since 2018, the 8th edition of the TNM staging system has been used to stage lung cancer based on local tumor extent (T), nodal involvement (N), and metastases (M). 18 F fluorodeoxyglucose (FDG) PET/CT, which combines functional and anatomic imaging, is the standard of care and an integral part of clinical staging of patients with lung cancer. Malignant pleural mesothelioma (MPM), the most common primary malignant pleural tumor affecting the pleura is staged with 8th edition of TNM staging for MPM. 18 F FDG PET/CT is indicated in select patients who are surgical candidates to identify locally advanced tumor, nodal metastases, or extrathoracic metastases, which may preclude surgery.

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

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

The value of the Standardized Uptake Value (SUV) and Metabolic Tumor Volume (MTV) in lung cancer

The diagnosis, staging and therapeutic monitoring of lung cancer were amongst the first applications for which the utility of FDG PET was documented and FDG PET/CT is now a routine diagnostic tool for clinical decision-making. As well as having high sensitivity for detection of disease sites, which provides critical information about stage, the intensity of uptake provides deeper biological characterization, while the burden of disease also has potential clinical significance. These disease characteristics can easily be quantified on delayed whole-body imaging as the maximum standardized uptake value (SUV_max) and metabolic tumor volume (MTV), respectively. There have been significant efforts to harmonize the measurement of these features, particularly within the context of clinical trials. Nevertheless, however calculated, in general, a high SUV_max and large MTV have been shown to have an adverse prognostic significance. Nevertheless, the use of these parameters in the interpretation and reporting of clinical scans remains inconsistent and somewhat controversial. This review details the current status of semi-quantitative FDG PET/CT in the evaluation of lung cancer.

Implications of the Harmonization of [18F]FDG-PET/CT Imaging for Response Assessment of Treatment in Radiotherapy Planning

The purpose of this work is to present useful recommendations for the use of [18F]FDG-PET/CT imaging in radiotherapy planning and monitoring under different versions of EARL accreditation for harmonization of PET devices. A proof-of-concept experiment designed on an anthropomorphic phantom was carried out to establish the most suitable interpolation methods of the PET images in the different steps of the planning procedure. Based on PET/CT images obtained by using these optimal interpolations for the old EARL accreditation (EARL1) and for the new one (EARL2), the treatment plannings of representative actual clinical cases were calculated, and the clinical implications of the resulting differences were analyzed. As expected, EARL2 provided smaller volumes with higher resolution than EARL1. The increase in the size of the reconstructed volumes with EARL1 accreditation caused high doses in the organs at risk and in the regions adjacent to the target volumes. EARL2 accreditation allowed an improvement in the accuracy of the PET imaging precision, allowing more personalized radiotherapy. This work provides recommendations for those centers that intend to benefit from the new accreditation, EARL2, and can help build confidence of those that must continue working under the EARL1 accreditation.