Prognostic and Predictive Values of Metabolic Parameters of 18F-FDG PET/CT in Patients With Non-Small Cell Lung Cancer Treated With Chemotherapy

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer

Objective.Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer.Approach.Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression.Main results.GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78,p= 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models (p= 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219,p< 0.004).Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

SFXN1 as a potential diagnostic and prognostic biomarker of LUAD is associated with 18F-FDG metabolic parameters

BACKGROUND

Sideroflexin 1 (SFXN1) has been discovered as a novel tumor marker for lung adenocarcinoma, but data on its importance in the development of lung adenocarcinoma is still limited. This study evaluated the correlation between SFXN1 and parameters related to 18F-flurodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), and further explored the role of SFXN1 in the value-added and glycolytic processes of LUAD.

METHOD

The expression and prognostic value of SFXN1 mRNA in LUAD were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data base. Retrospective analysis of 18F-FDG PET imaging and metabolic parameters in 42 patients to explore the relationship between the expression of SFXN1 and glucose metabolism levels in lung adenocarcinoma and its clinical significance. H1975 cells were selected as the in vitro research object, and the biological effects of SFXN1 on LUAD were further elucidated through Edu proliferation assay, CCK8 activity assay, wound healing experiment, and cell flow cytometry.

RESULT

SFXN1 is highly expressed in various tumors, including LUAD, and its high expression can serve as an independent predictor of overall survival in lung adenocarcinoma. In addition, the expression of SFXN1 in LUAD was significantly correlated with 18F-FDG PET/CT parameters: maximum and average standardized uptake values (SUVmax and SUVmean), as well as total lesion glycolysis (TLG) (rho = 0.574, 0.589, and 0.338, p < 0.05), which can predict the expression of SFXN1 with an accuracy of 0.934. In vitro functional experiments have shown that knocking down SFXN1 inhibits the proliferation and migration of LUAD cells, promotes cell apoptosis, and may inhibit tumor activity by regulating the expression of glycolytic related genes SLC2A1, HK2, GPI, ALDOA, GAPDH, ENO1, PKM, and LDHA.

CONCLUSION

The overexpression of SFXN1 is closely related to FDG uptake, and SFXN1, as a promising prognostic biomarker, may mediate the development of LUAD through the glycolytic pathway.

FDG PET/CT Tumor Dissemination Characteristic Predicts the Outcome of First-Line Systemic Therapy in Non-small Cell Lung Cancer

RATIONALE AND OBJECTIVES

To explore the correlation between the tumor dissemination characteristic at 18F-fluoro-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images and the outcome of first-line systemic therapy for stage IV non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

The current retrospective study included 101 NSCLC patients receiving first-line systemic therapy with baseline 18F-FDG PET/CT images available. The distance between the two lesions that were the farthest apart was defined as Dmax to calculate the tumor dissemination. The tumor metabolic volume (MTV) of the primary tumor and the MTV of the whole-body tumor lesions (MTVwb) were calculated using 18F-FDG PET/CT imaging. The Kaplan-Meier survival analyses and Cox predictive model were performed to assess the relationship between the parameters and survival.

RESULTS

Dmax and MTVwb were independent prognostic factors for overall survival (OS) (p = 0.019 and p = 0.011, respectively) and progression-free survival (PFS) (p = 0.043 and p = 0.009, respectively). Poor PFS and OS were associated with high MTVwb (>54.0 cm3) and high Dmax (>48.5 cm) (p = 0.006 and p = 0.008, respectively). When MTVwb and Dmax were combined, three risk groups were stratified with no (score 0), one (score 1), or two (score 2) factors (p < 0.001 for PFS, p < 0.001 for OS). The group with a score of 0 had a considerably longer PFS and OS than those who received a score of 1 or 2 (PFS: 61.1%, 43.5%, and 21.1%, respectively, OS: 77.8%, 54.3%, and 36.8%, respectively).

CONCLUSION

The combination of tumor dissemination characteristic (Dmax) and tumor burden (MTVwb) can further improve the prognosis stratification of NSCLC.

Tumor to liver maximum standardized uptake value ratio of FDG-PET/CT parameters predicts tumor treatment response and survival of stage III non-small cell lung cancer

BACKGROUND

To assess the predictive values of primary tumor FDG uptake for patients with inoperable stage III non-small cell lung cancer (NSCLC) after concurrent chemoradiotherapy (CCRT).

METHODS

A total of 107 patients with diagnosis of stage III NSCLC and CCRT were enrolled. The tumor maximum uptake value (SUVmax) was standardized by calculating several ratios between tumor and each background tissues. The receiver operating characteristics curve (ROC) was used to compare the predictive power of prognostic models. The tumor objective response rate (ORR) and overall survival (OS) were compared and analyzed by the Kaplan-Meier method and univariate and multivariate Cox regression models.

RESULTS

The areas under ROC curve (AUCs) ranged from 0.72 to 0.81 among these tumor SUVmax and standardized SUVmax ratios, and the tumor SUVmax and tumor SUVmax-to-liver SUVmean ratio (TLMR) were more predictive of ORR (AUC, 0.81; 95% CI, 0.73-0.88 for tumor SUVmax and AUC, 0.84; 95%CI, 0.76-0.91 for TLMR) than any of other SUVmax ratios. The patients with lower tumor SUVmax, SUVmean and SUVmax ratios had a significantly better OS than those with their corresponding higher ones. Moreover, both univariate and multivariable analyses revealed that TLMR was significantly associated with better ORR and OS after adjustment with other prognostic variables.

CONCLUSIONS

TLMR, a standardized tumor SUVmax, was an independent prognostic predictor for tumor ORR and OS of patients with stage III NSCLC after CCRT.

Predictive value of baseline metabolic tumor volume for non-small-cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis

Background

Immune checkpoint inhibitors (ICIs) have emerged as a promising treatment option for advanced non-small-cell lung cancer (NSCLC) patients, highlighting the need for biomarkers to identify responders and predict the outcome of ICIs. The purpose of this study was to evaluate the predictive value of baseline standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) derived from 18F-FDG-PET/CT in advanced NSCLC patients receiving ICIs.

Methods

PubMed and Web of Science databases were searched from January 1st, 2011 to July 18th, 2022, utilizing the search terms “non-small-cell lung cancer”, “PET/CT”, “standardized uptake value”, “metabolic tumor volume”, “ total lesion glycolysis”, and “immune checkpoint inhibitors”. Studies that analyzed the association between PET/CT parameters and objective response, immune-related adverse events (irAEs) and prognosis of NSCLC patients treated with ICIs were included. We extracted the hazard ratio (HR) with a 95% confidence interval (CI) for progression-free survival (PFS) and overall survival (OS). We performed a meta-analysis of HR using Review Manager v.5.4.1.

Results

Sixteen studies were included for review and thirteen for meta-analysis covering 770 patients. As for objective response and irAEs after ICIs, more studies with consistent assessment methods are needed to determine their relationship with MTV. In the meta-analysis, low SUVmax corresponded to poor PFS with a pooled HR of 0.74 (95% CI, 0.57-0.96, P=0.02). And a high level of baseline MTV level was related to shorter PFS (HR=1.45, 95% CI, 1.11-1.89, P<0.01) and OS (HR, 2.72; 95% CI, 1.97-3.73, P<0.01) especially when the cut-off value was set between 50-100 cm³. SUVmean and TLG were not associated with the prognosis of NSCLC patients receiving ICIs.

Conclusions

High level of baseline MTV corresponded to shorter PFS and OS, especially when the cut-off value was set between 50-100 cm³. MTV is a potential predictive value for the outcome of ICIs in NSCLC patients.

Prognostic Potential of Metabolic Activity on 18F-FDG Accumulation in Advanced NSCLC Receiving Combining Chemotherapy Plus PD-1 Blockade

Combined chemotherapy plus programmed death-1 (PD-1) blockade is an established treatment against patients with advanced non-small cell lung cancer (NSCLC). However, a promising predictor besides programmed death ligand-1 expression remains uncertain. We examined the prognostic significance of baseline 18F-FDG-positron emission tomography for predicting first-line combined chemotherapy plus PD-1 blockade in NSCLC patients. Forty-five patients with advanced NSCLC who received 18F-FDG-positron emission tomography immediately before combined platinum-based chemotherapy with PD-1 blockade as first-line setting were eligible for this study, and assessment of maximum of standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on 18F-FDG uptake was performed. The objective response rate, median progression-free survival, and overall survival were 51.2%, 206 days, and 681 days, respectively. High SUVmax, TLG, and MTV significantly correlated with age and performance status (PS), C-reactive protein (CRP), and PS, CRP, albumin, and baseline tumor size, respectively. Univariate analysis identified albumin, TLG and MTV as significant predictors of progression-free survival, and CRP, albumin, TLG and MTV as significant factors for predicting overall survival. High TLG was confirmed as an independent factor associated with poor prognosis in multivariate analysis. In particular, TLG is identified as the most powerful predictor in patients with good PS, adenocarcinoma, programmed death ligand-1≥1%, and low baseline tumor size. The tumor metabolic volume by MTV and TLG at pretreatment was clarified as a significant predictor for combined chemotherapy with PD-1 blockade, but not maximal glycolytic level by SUVmax.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Background

Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

Methods

A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction.

Findings

1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features.

Interpretation

CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care.

Funding

NIH NHLBI training grant (5T35HL094308-12, John Sollee).

Glucose Metabolism Reprogramming of Primary Tumor and the Liver Is Associated With Disease-Free Survival in Patients With Early NSCLC

Purpose

Tumor promote disease progression by reprogramming their metabolism and that of distal organs, so it is of great clinical significance to study the changes in glucose metabolism at different tumor stages and their effect on glucose metabolism in other organs.

Methods

A retrospective single-centre study was conducted on 253 NSCLC (non-small cell lung cancer) patients with negative lymph nodes and no distant metastasis. According to the AJCC criteria, the patients were divided into different groups based on tumor size: stage IA, less than 3 cm (group 1, n = 121); stage IB, greater than 3-4 cm (group 2, n = 64); stage IIA, greater than 4-5 cm (group 3, n = 36); and stage IIB, greater than 5-7 cm (group 4, n = 32). All of the patients underwent baseline ¹⁸F-FDG PET/CT scans, and the primary lesion SUVmax (maximum standardized uptake value), liver SUVmean (mean standardized uptake value), spleen SUVmean, TLR (Tumor-to-liver SUV ratio) and TSR (Tumor-to-spleen SUV ratio) were included in the study, combined with clinical examination indicators to evaluate DFS (disease free survival).

Results

In NSCLC patients, with the increase in the maximum diameter of the tumor, the SUVmax of the primary lesion gradually increased, and the SUVmean of the liver gradually decreased. The primary lesion SUVmax, liver SUVmean, TLR and TSR were related to disease recurrence or death. The best predictive parameters were different when the tumor size differed. SUVmax had the highest efficiency when the tumor size was less than 4 cm (AUC:0.707 (95% CI, 0.430-0.984) tumor size < 3 cm), (AUC:0.726 (95% CI, 0.539-0.912) tumor size 3-4 cm), liver SUVmean had the highest efficiency when the tumor size was 4-5 cm (AUC:0.712 (95% CI, 0.535-0.889)), and TLR had the highest efficiency when the tumor size was 5-7 cm [AUC:0.925 (95%CI, 0.820-1.000)].

Conclusions

In patients with early NSCLC, glucose metabolism reprogramming occurs in the primary lesion and liver. With the increase in tumor size, different metabolic parameters should be selected to evaluate the prognosis of patients.

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.

Prognostic Significance of SUVmax Combined With Lactate Dehydrogenase in Advanced Lung Cancer Patients Treated With Immune Checkpoint Inhibitor Plus Chemotherapy: A Retrospective Study

Purpose

This research aims to investigate the predictive capacity of PET/CT quantitative parameters combined with haematological parameters in advanced lung cancer patients treated with immune checkpoint inhibitor (ICI) plus chemotherapy.

Methods

A total of 120 patients who underwent ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) were enrolled before therapy. The following parameters were calculated: the maximum, mean, and peak standardized uptake value (SUVmax, SUVmean, and SUVpeak, respectively); total tumour volume (MTV) and total lesion glycolysis (TLG); and whole-body metabolic values (MTVwb, TLGwb, SUVmeanwb, and SUVmaxwb). Lactate dehydrogenase (LDH) levels, absolute neutrophil count, absolute platelet count, albumin levels and derived neutrophil to lymphocyte ratio (dNLR) were also computed. The associations between the variables and therapy outcome (evaluated by iRECIST) were analyzed.

Results

Based on iRECIST, 32 of 120 patients showed iPD, 43 iSD, 36 iPR and 9 iCR. Multivariate analysis found that SUVmax, MTVwb, LDH and absolute platelet count were associated with treatment response (P =0.015, P =0.005, P <0.001 and P =0.015, respectively). Kaplan-Meier survival analyses showed that SUVmax ≥11.42 and LDH ≥245 U/L were associated with shorter OS (P = 0.001 and P = 0.004, respectively). Multivariate Cox regression revealed that SUVmax and LDH alone were not correlated with survival prognosis (p>0.05), but the combination of SUVmax and LDH was independently associated with OS (P=0.015, P=0.001, respectively). The median survival time (MST) for the low (LDH<245 and SUVmax<11.42), intermediate(LDH<245 or SUVmax<11.42), and high(SUVmax≥11.42 and LDH≥245) groups was 24.10 months (95% CI: 19.43 to 28.77), 17.41 months (95% CI: 15.83 to 18.99), and 13.76 months (95% CI: 12.51 to 15.02), respectively.

Conclusion

This study identified that SUVmax plus LDH correlated with the survival outcome in patients with advanced lung cancer receiving PD-1/PD-L1 blockade plus chemotherapy.

On the performance of lung nodule detection, segmentation and classification

Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.

Enhanced glucose metabolism mediated by CD147 is associated with 18 F-FDG PET/CT imaging in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is one of the most deadly thoracic tumors. Reprogrammed glycolytic metabolism is a hallmark of cancer cells and significantly affects several cellular functions. In the current study, we aimed to investigate cluster of differentiation 147 (CD147)‐mediated glucose metabolic regulation in LUAD and its association with ¹⁸F‐FDG PET/CT imaging.

Methods

The expression profile and prognostic potential of CD147 in LUAD were analyzed using UALCAN and a Kaplan‐Meier plotter. Tissue immunohistochemical analyses and PET metabolic parameters were used to identify the relationship between CD147 expression and reprogrammed glycolysis. The role of CD147 in glucose metabolic reprogramming was assessed by radioactive uptake of ¹⁸F‐FDG through γ‐radioimmunoassays in vitro and micro‐PET/CT imaging in vivo. Western blotting assays were used to determine the expression level of monocarboxylate transporter 1 (MCT1) and MCT4 in established human LUAD cell lines (ie, HCC827 and H1975) with different CD147 expression levels via lentiviral transduction.

Results

CD147 was highly expressed in LUAD. A significant positive correlation existed between CD147 expression and PET metabolic parameters(SUVmax,SUVmean, SUVpeak). CD147 could promote radioactive uptake of ¹⁸F‐FDG in vitro and in vivo, suggesting the ability of CD147 to enhance glycolytic metabolism. Furthermore, as an obligate chaperone for MCT1 and MCT4, CD147 positively correlated with MCT1 and MCT4 expression in LUAD tissues and established cell lines with different CD147 expression.

Conclusions

Our study revealed that CD147 is a promising novel target for LUAD treatment and CD147‐mediated glucose metabolism demonstrated its contribution to the predictive role of ¹⁸F‐FDG PET/CT imaging for targeted therapeutic efficacy.