Lung cancer: Morphological and functional approach to screening, staging and treatment planning

The value of PET/CT radiomic texture analysis of primary mass and mediastinal lymph node on survival in patients with non-small cell lung cancer

OBJECTIVE

This study was designed to determine the potential prognostic value of radiomic texture analysis and metabolic-volumetric parameters obtained from positron emission tomography (PET) in primary mass and metastatic hilar/mediastinal lymph nodes in stage 2-3 non-small cell lung cancer (NSCLC).

METHODS

Images of patients diagnosed with stage 2-3 NSCLC who underwent 18F-FDG PET/CT imaging for staging up to 4 weeks before the start of treatment were evaluated using LIFEx software. Volume of interest (VOI) was generated from the primary tumor and metastatic lymph node separately, and volumetric and textural features were obtained from these VOIs. The relationship between the parameters obtained from PET of primary mass and the metastatic hilar/mediastinal lymph nodes with overall survival (OS) and progression-free survival (PFS) was analyzed.

RESULTS

When radiomic features, gender and stage obtained from lymph nodes were evaluated by Cox regression analysis; GLCM_correlation (p: 0.033, HR: 4,559, 1.660-12.521, 95% CI), gender and stage were determined as prognostic factors predicting OS. In predicting PFS; stage, smoking and lymph node MTV (p: 0.033, HR: 1.008, 1.001-1.016, 95% CI) were determined as prognostic factors. However, the radiomic feature of the primary tumor could not show a significant relationship with either OS or PFS.

CONCLUSIONS

In a retrospective cohort of NSCLC patients with Stage 2 and 3 disease, volumetric and radiomic texture characteristics obtained from metastatic lymph nodes were associated with PFS and OS. Tumor heterogeneity, defined by radiomic texture features of 18 F-FDG PET/CT images, may provide complementary prognostic value in NSCLC.

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.

Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer

Background

The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (¹⁸F-FDG PET/CT) images based on a "rough" volume of interest (VOI) containing the tumor instead of its accurate delineation, which is a significant time-consuming bottleneck of radiomics analyses.

Methods

A cohort of 138 patients with stage II-III NSCLC treated with radiochemotherapy recruited retrospectively (n = 87) and prospectively (n = 51) was used. Two approaches were compared: firstly, the radiomic features were extracted from the delineated primary tumor volumes in both PET (using the automated fuzzy locally adaptive Bayesian, FLAB) and CT (using a semi-automated approach with 3D Slicer™) components. Both delineations were carried out within previously manually defined "rough" VOIs containing the tumor and the surrounding tissues, which were exploited for the second approach: the same features were extracted from this alternative VOI. Both sets for features were then combined with the clinical variables and processed through the same machine learning (ML) pipelines using the retrospectively recruited patients as the training set and the prospectively recruited patients as the testing set. Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). The resulting models were compared in terms of balanced accuracy, sensitivity, and specificity.

Results

Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89 vs. 0.88 and 0.78 vs. 0.77).

Conclusion

Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.

Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer

Machine learning (ML) algorithms for selecting and combining radiomic features into multiparametric prediction models have become popular; however, it has been shown that large variations in performance can be obtained by relying on different approaches. The purpose of this study was to evaluate the potential benefit of combining different algorithms into an improved consensus for the final prediction, as it has been shown in other fields. Methods: The evaluation was carried out in the context of the use of radiomics from ¹⁸F-FDG PET/CT images for predicting outcome in stage II-III Non-Small Cell Lung Cancer. A cohort of 138 patients was exploited for the present analysis. Eighty-seven patients had been previously recruited retrospectively for another study and were used here for training and internal validation. We also used data from prospectively recruited patients (n = 51) for testing. Three different machine learning pipelines relying on embedded feature selection were trained to predict overall survival (OS) as a binary classification: Support Vector machines (SVMs), Random Forests (RFs), and Logistic Regression (LR). Two different clinical endpoints were investigated: median OS or OS shorter than 6 months. The fusion of the three approaches was implemented using two different strategies: majority voting on the binary outputs or averaging of the output probabilities. Results: Our results confirm previous findings, highlighting that different ML pipelines select different sets of features and reach different classification performances (accuracy in the testing set ranging between 63% and 67% for median OS, and between 75% and 80% for OS < 6 months). Generating a consensus improved the performance for both endpoints; with the probabilities averaging strategy outperforming the majority voting (accuracy of 78% vs. 71% for median OS and 89 vs. 84% for OS < 6 months). Overall, the performance of these radiomic-based models outperformed the standard clinical staging in both endpoints (accuracy of 58% and 53% accuracy in the testing set for each endpoint). Conclusion: Although obtained in a small cohort of patients, our results suggest that a consensus of machine learning algorithms can improve performance in the context of radiomics. The resulting prognostic stratification in the prospective testing cohort is higher than when relying on the clinical stage. This could be of interest for clinical practice as it could help to identify patients with higher risk amongst stage II and III patients, who could benefit from intensified treatment and/or more frequent follow-up after treatment.

[(18)F]Fluorodeoxyglucose-positron emission tomography screening for lung cancer: a systematic review and meta-analysis

Rationale and objectives: Although low-dose computed tomography (CT) is a recommended modality for lung cancer screening in high-risk populations, the role of other modalities, such as [¹⁸F]fluorodeoxyglucose-positron emission tomography (PET), is unclear. We conducted a systematic review to describe the role of PET in lung cancer screening. Materials and methods: A systematic review was conducted by reviewing primary studies focusing on PET screening for lung cancer until July 2012. Two independent reviewers identified studies that were compatible for inclusion/exclusion criteria. The analysis was restricted to English and included studies published since 2000. A descriptive analysis was used to summarize the results, and the pooled diagnostic performance of selective PET screening was calculated by weighted average using individual sample sizes. Results: Among the identified studies (n = 3497), 12 studies were included for analysis. None of the studies evaluated the efficacy of primary PET screening specific to lung cancer. Eight studies focused on primary PET screening for all types of cancer; the detection rates of lung cancer were low. Four studies reported evidence of lung cancer screening programs with selective PET, in which the estimated pooled sensitivity and specificity was 83% and 91%, respectively. Conclusions: The role of primary PET screening for lung cancer remains unknown. However, PET has high sensitivity and specificity as a selective screening modality. Further studies must be conducted to evaluate the use of PET or PET/computed tomography screening for high-risk populations, preferably using randomized trials or prospective registration. Advances in knowledge: Our meta-analysis indicates that PET has high sensitivity and specificity as a selective screening modality.

MALDI-MS-Based Profiling of Serum Proteome: Detection of Changes Related to Progression of Cancer and Response to Anticancer Treatment

Mass spectrometry-based analyses of the low-molecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and classification of cancer. Several published studies have shown that multipeptide signatures selected in numerical tests have potential values for diagnostics of different types of cancer. However due to apparent problems with standardization of methodological details, both experimental and computational, none of the proposed peptide signatures analyzed directly by MALDI/SELDI-ToF spectrometry has been approved for routine diagnostics. Noteworthy, several components of proposed cancer signatures, especially those characteristic for advanced cancer, were identified as fragments of blood proteins involved in the acute phase and inflammatory response. This indicated that among cancer biomarker candidates to be possibly identified by serum proteome profiling were rather those reflecting overall influence of a disease (and the therapy) upon the human organism, than products of cancer-specific genes. Current paper focuses on changes in serum proteome that are related to response of patient's organism to progressing malignancy and toxicity of anticancer treatment. In addition, several methodological issues that affect robustness and interlaboratory reproducibility of MS-based serum proteome profiling are discussed.

Preliminary study on the evaluation of olfactory neuroblastoma using PET/CT

PURPOSE

Because of paucity of data available regarding the utility of PET/CT in the diagnosis and staging of patients with olfactory neuroblastoma (ONB), we retrospectively analyzed the efficacy of PET/CT in 9 patients with ONB.

MATERIALS AND METHODS

Whole-body F-18 FDG PET/CT was performed in 7 patients with newly diagnosed ONB, as well as in 1 patient with recurrence and in 1 patient with remnant tumor. Regional C-11 choline (C-11 CHO) PET/CT was performed in 2 patients with negative F-18 FDG scans. The lesion with intense radiotracer uptake was suggested as positive for tumor and the results of PET/CT were compared with conventional staging examinations.

RESULTS

F-18 FDG PET/CT was positive in 7/9 (77.7%) patients with ONB. In 2 patients with negative F-18 FDG, the lesions were C-11CHO avid. Both the primary tumors and its invasions were clearly delineated by F-18 FDG or C-11 CHO PET/CT. SUVmax of F-18 FDG in the primary tumor was 6.37 ± 4.22 and did not correlate with lesion size (F-18 FDG/size: r = 0.097, P = 0.820). Whole-body F-18 FDG PET/CT detected parapharyngeal and cervical lymph node metastases in 2 patients, lung metastases in 1 patient, liver metastases in 1 patients, and bone metastases in 2 patients. PET/CT altered the stages of 3 of 9 patients with upstaging in 2 patients and downstaging in 1 patient.

CONCLUSIONS

PET/CT may be useful for the diagnosis and staging of ONB.