2-[18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer

OBJECTIVES

To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC).

METHODS

A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA).

RESULTS

A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively.

CONCLUSION

The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC.

CLINICAL RELEVANCE STATEMENT

A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice.

KEY POINTS

• The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

Frontiers and hotspots of 18F-FDG PET/CT radiomics: A bibliometric analysis of the published literature

Objective

To illustrate the knowledge hotspots and cutting-edge research trends of ¹⁸F-FDG PET/CT radiomics, the knowledge structure of was systematically explored and the visualization map was analyzed.

Methods

Studies related to ¹⁸F-FDG PET/CT radiomics from 2013 to 2021 were identified and selected from the Web of Science Core Collection (WoSCC) using retrieval formula based on an interview. Bibliometric methods are mainly performed by CiteSpace 5.8.R3, which we use to build knowledge structures including publications, collaborative and co-cited studies, burst analysis, and so on. The performance and relevance of countries, institutions, authors, and journals were measured by knowledge maps. The research foci were analyzed through research of keywords, as well as literature co-citation analysis. Predicting trends of ¹⁸F-FDG PET/CT radiomics in this field utilizes a citation burst detection method.

Results

Through a systematic literature search, 457 articles, which were mainly published in the United States (120 articles) and China (83 articles), were finally included in this study for analysis. Memorial Sloan-Kettering Cancer Center and Southern Medical University are the most productive institutions, both with a frequency of 17. ¹⁸F-FDG PET/CT radiomics–related literature was frequently published with high citation in European Journal of Nuclear Medicine and Molecular Imaging (IF9.236, 2020), Frontiers in Oncology (IF6.244, 2020), and Cancers (IF6.639, 2020). Further cluster profile of keywords and literature revealed that the research hotspots were primarily concentrated in the fields of image, textural feature, and positron emission tomography, and the hot research disease is a malignant tumor. Document co-citation analysis suggested that many scholars have a co-citation relationship in studies related to imaging biomarkers, texture analysis, and immunotherapy simultaneously. Burst detection suggests that adenocarcinoma studies are frontiers in ¹⁸F-FDG PET/CT radiomics, and the landmark literature put emphasis on the reproducibility of ¹⁸F-FDG PET/CT radiomics features.

Conclusion

First, this bibliometric study provides a new perspective on ¹⁸F-FDG PET/CT radiomics research, especially for clinicians and researchers providing scientific quantitative analysis to measure the performance and correlation of countries, institutions, authors, and journals. Above all, there will be a continuing growth in the number of publications and citations in the field of ¹⁸F-FDG PET/CT. Second, the international research frontiers lie in applying ¹⁸F-FDG PET/CT radiomics to oncology research. Furthermore, new insights for researchers in future studies will be adenocarcinoma-related analyses. Moreover, our findings also offer suggestions for scholars to give attention to maintaining the reproducibility of ¹⁸F-FDG PET/CT radiomics features.

18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

Objective

Lymph node metastasis (LNM) is not only one of the important factors affecting the prognosis of gastric cancer but also an important basis for treatment decisions. The purpose of this study was to investigate the value of the radiomics nomogram based on preoperative ¹⁸F-deoxyglucose (FDG) PET/CT primary lesions and clinical risk factors for predicting LNM in gastric cancer (GC).

Methods

We retrospectively analyzed radiomics features of preoperative ¹⁸F-FDG PET/CT images in 224 gastric cancer patients from two centers. The prediction model was developed in the training cohort (n = 134) and validated in the internal (n = 59) and external validation cohorts (n = 31). The least absolute shrinkage and selection operator (LASSO) regression was used to select features and build radiomics signatures. The radiomics feature score (Rad-score) was calculated and established a radiomics signature. Multivariate logistic regression analysis was used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was used to select the optimal model parameters to construct a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.

Results

There was no significant difference between the internal verification and external verification of the clinical data of patients (all p > 0.05). The areas under the curve (AUCs) (95% CI) for predicting LNM based on the ¹⁸F-FDG PET/CT radiomics signature in the training cohort, internal validation cohort, and external validation cohort were 0.792 (95% CI: 0.712–0.870), 0.803 (95% CI: 0.681–0.924), and 0.762 (95% CI: 0.579–0.945), respectively. Multivariate logistic regression showed that carbohydrate antigen (CA) 19-9 [OR (95% CI): 10.180 (1.267–81.831)], PET/CT diagnosis of LNM [OR (95% CI): 6.370 (2.256–17.984)], PET/CT Rad-score [OR (95% CI): 16.536 (5.506–49.660)] were independent influencing factors of LNM (all p < 0.05), and a radiomics nomogram was established based on those factors. The AUCs (95% CI) for predicting LNM were 0.861 (95% CI: 0.799–0.924), 0.889 (95% CI: 0.800–0.976), and 0.897 (95% CI: 0.683–0.948) in the training cohort, the internal validation cohort, and the external validation cohort, respectively. Decision curve analysis (DCA) indicated that the ¹⁸F-FDG PET/CT-based radiomics nomogram has good clinical utility.

Conclusions

Radiomics nomogram based on the primary tumor of ¹⁸F-FDG PET/CT could facilitate the preoperative individualized prediction of LNM, which is helpful for risk stratification in GC patients.

Methyltransferase-like 3 facilitates lung cancer progression by accelerating m6A methylation-mediated primary miR-663 processing and impeding SOCS6 expression

OBJECTIVE

Lung cancer (LC) remains a threatening health issue worldwide. Methyltransferase-like protein 3 (METTL3) is imperative in carcinogenesis via m6A modification of microRNAs (miRNAs). This study estimated the effect of METTL3 in LC by regulating m6A methylation-mediated pri-miR-663 processing.

METHODS

miR-663 expression in 4 LC cell lines and normal HBE cells was determined using RT-qPCR. A549 and PC9 LC cells selected for in vitro studies were transfected with miR-663 mimics or inhibitor. Cell viability, migration, invasion, proliferation, and apoptosis were detected by CCK-8, Transwell, EdU, and flow cytometry assays. The downstream target genes and binding sites of miR-663 were predicted via Starbase database and validated by dual-luciferase assay. LC cells were delivered with oe-METTL3/sh-METTL3. Crosslinking between METTL3 and DGCR8 was verified by co-immunoprecipitation. Levels of m6A, miR-663, and pri-miR-663 were measured by m6A dot blot assay and RT-qPCR. m6A modification of pri-miR-663 was verified by Me-RIP assay. Finally, the effects of METTL3 in vivo were ascertained by tumor xenograft in nude mice.

RESULTS

miR-663 was upregulated in LC cells, and miR-663 overexpression promoted cell proliferation, migration, invasion, and inhibited apoptosis, but miR-663 knockdown exerted the opposite effects. miR-663 repressed SOCS6 expression. SOCS6 overexpression annulled the promotion of miR-663 on LC cell growth. METTL3 bound to DGCR8, and METTL3 silencing elevated the levels of pri-miR-663 and m6A methylation-modified pri-miR-663, and suppressed miR-663 maturation and miR-663 expression. METTL3 facilitated tumor growth in mice through the miR-663/SOCS6 axis.

CONCLUSION

METTL3 promotes LC progression by accelerating m6A methylation-mediated pri-miR-663 processing and repressing SOCS6.

ImmunoPET: Antibody-Based PET Imaging in Solid Tumors

Immuno-positron emission tomography (immunoPET) is a molecular imaging modality combining the high sensitivity of PET with the specific targeting ability of monoclonal antibodies. Various radioimmunotracers have been successfully developed to target a broad spectrum of molecules expressed by malignant cells or tumor microenvironments. Only a few are translated into clinical studies and barely into clinical practices. Some drawbacks include slow radioimmunotracer kinetics, high physiologic uptake in lymphoid organs, and heterogeneous activity in tumoral lesions. Measures are taken to overcome the disadvantages, and new tracers are being developed. In this review, we aim to mention the fundamental components of immunoPET imaging, explore the groundbreaking success achieved using this new technique, and review different radioimmunotracers employed in various solid tumors to elaborate on this relatively new imaging modality.

Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules

Objectives

This study aimed to evaluate the ability of ¹⁸fluorine-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN).

Methods

Data of 48 patients with SPN detected on ¹⁸F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC).

Results

The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%).

Conclusion

Machine learning based on ¹⁸F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.

Interventions of Advanced Lung Cancer Patient Receiving Chemotherapy by Computed Tomography Image Information Data Analysis-Based Soothing Care Plans

The objective of this study was to investigate the intervention effect of computed tomography (CT) image information data on patients with advanced lung cancer treated with chemotherapy under palliative care program. The research subjects were 60 patients with advanced lung cancer who received palliative care in our hospital from January 1, 2019, to January 1, 2021. All patients were grouped according to the evaluation criteria of solid tumor efficacy, including 28 patients in the remission group and 32 patients in the nonremission group. Texture analysis was performed on the CT images of the two groups of patients. The gray-scale cooccurrence matrix parameters, the maximum diameter of the lesion, and the CT value of the CT images of the two groups of patients before and after palliative care were compared. The results showed that after the palliative care, the combined mean, combined energy, and inverse moment of the three gray cooccurrence matrix parameters of the two groups of patients were decreased, and the combined entropy and contrast were increased. The absolute value of the gray-scale cooccurrence matrix Δ parameter of the patients in the remission group was greater than that in the nonremission group. The Δ combined entropy, Δ contrast, and Δ correlation of the two groups of patients were significantly different, and the difference in Δ contrast was the largest. It suggested that the gray-scale cooccurrence matrix parameter can evaluate the effect of soothing care, and the contrast was the best evaluation parameter. The maximum diameter of the lesions in the remission group before and after palliative care was reduced by 1.23 cm, and the degree of reduction was significantly better. The CT value was reduced by 6.22 HU, and the degree of reduction was significantly higher than that in the nonremission group. There was a significant difference in the data between the two groups (P < 0.05). Therefore, the CT image information data had a better evaluation effect on patients with advanced lung cancer under the palliative care program and can be applied to the clinical evaluation of the palliative care effect, which had good clinical value.

A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions

Background

This is a pilot study of radiomics based on ⁶⁸Ga-NOTA-PRGD2 [NOTA-PEG4-E[c(RGDfK)]2)] and ¹⁸F-FDG PET/CT to (i) evaluate the diagnostic efficacy of radiomics features of ⁶⁸Ga-NOTA-PRGD2 PET in the differential diagnosis of benign and malignant pulmonary space-occupying lesions and (ii) compare the diagnostic efficacy of multi-modality and multi-probe images.

Methods

We utilized a dataset of 48 patients who participated in ⁶⁸Ga-NOTA-PRGD2 PET/CT and ¹⁸F-FDG PET/CT clinical trials to extract image features and evaluate their diagnostic efficacy in the differentiation of benign and malignant lesions by the Mann-Whitney U test. After feature selection with sequential forward selection, random forest models were developed with tenfold cross-validation. The diagnostic performance of models based on different image features was visualized by receiver operating characteristic (ROC) curves and compared by permutation tests.

Results

Fourteen of the ⁶⁸Ga-NOTA-PRGD2 PET features between benign and malignant pulmonary space-occupying lesions had significant differences (P<0.05, Mann-Whitney U test). Eighteen of the ⁶⁸Ga-NOTA-PRGD2 PET features demonstrated higher AUC values than all CT features in the differential diagnosis of pulmonary lesions. The AUC value (0.908) ​​of the three-modal feature model was significantly higher (P<0.05, permutation test) than those of the single- and dual-modal models.

Conclusion

⁶⁸Ga-NOTA-PRGD2 PET features have better diagnostic capacity than CT features for pulmonary space-occupying lesions. The combination of multi-modality and multi-probe images can improve the diagnostic efficiency of models. Our preliminary clinical hypothesis of using radiomics based on ⁶⁸Ga-NOTA-PRGD2 PET images and multimodal images as a diagnostic tool warrants further validation in a larger multicenter sample size.

[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.

Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter

Background

To investigate the value of ¹⁸F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm).

Methods

A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA).

Results

According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93–0.97) in the training group and 0.94 (95% CI: 0.89–0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM.

Conclusions

The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00895-x.

Pretherapy 18F-fluorodeoxyglucose positron emission tomography/computed tomography robust radiomic features predict overall survival in non-small cell lung cancer

OBJECTIVE

To extract robust radiomic features from staging positron emission tomography/computed tomography (18F- fluroodeoxyglucose PET/CT) in patients with non-small cell lung cancer from different segmentation methods and to assess their association with 2-year overall survival.

METHODS

Eighty-one patients with stage I-IV non-small cell lung cancer were included. All patients underwent a pretherapy 18F-FDG PET/CT. Primary tumors were delineated using four different segmentation methods: method 1, manual; method 2: manual with peripheral 1 mm erosion; method 3: absolute threshold at standardized uptake value (SUV) 2.5; and method 4: relative threshold at 40% SUVmax. Radiomic features from each method were extracted using Image Biomarker Standardization Initiative-compliant process. The study cohort was divided into two groups (exploratory and testing) in a ratio of 1:2 (n = 25 and n = 56, respectively). Exploratory cohort was used to identify robust radiomic features, defined as having a minimum concordance correlation coefficient ≥0.75 among all the 4-segmentation methods. The resulting texture features were evaluated for association with 2-year overall survival in the testing cohort (n = 56). All patients in the testing cohort had a follow-up for 2 years from the date of staging 18F-FDG PET/CT scan or till death. Cox proportional hazard models were used to evaluate the independent prognostic factors.

RESULTS

Exploratory and validation cohorts were equivalent regarding their basic characteristics (age, sex, and tumor stage). Ten radiomic features were deemed robust to the described four segmentation methods: SUV SD, SUVmax, SUVQ3, SUVpeak in 0.5 ml, total lesion glycolysis, histogram entropy log 2, histogram entropy log 10, histogram energy uniformity, gray level run length matrix-gray level non-uniformity, and gray level zone length matrix-gray level non-uniformity. At the end of 2-year follow-up, 41 patients were dead and 15 were still alive (overall survival = 26.8%; median survival = 14.7 months, 95% confidence interval: 10.2-19.2 months). Three texture features, regardless the segmentation method, were associated with 2-year overall survival: total lesion glycolysis, gray level run length matrix_gray level non-uniformity, and gray level zone length matrix_run-length non-uniformity. In the final Cox-regression model: total lesion glycolysis, and gray level zone length matrix_gray level non-uniformity were independent prognostic factors. The quartiles from the two features were combined with clinical staging in a prognostic model that allowed better risk stratification of patients for overall survival.

CONCLUSION

Ten radiomic features were robust to segmentation methods and two of them (total lesion glycolysis and gray level zone length matrix_gray level non-uniformity) were independently associated with 2-year overall survival. Together with the clinical staging, these features could be utilized towards improved risk stratification of lung cancer patients.

Haralick texture features extracted from Ga-68 PSMA PET/CT to differentiate normal prostate from prostate cancer: a feasibility study

OBJECTIVE

Role of texture parameters on the basis of Ga-68 PSMA PET/CT in prostate cancer (Pca) is largely unexplored. Present work done is a preliminary study that aims to evaluate the role of Haralick texture features on the basis of Ga-68 PSMA PET/CT in Pca in which texture features were used to differentiate between normal prostate and Pca tissue.

METHODS

The study retrospectively enrolled patients in two groups: group 1 included 30 patients with biopsy-proven adenocarcinoma prostate and median age 64 years (range: 50-82 years) who underwent baseline Ga-68 PSMA PET/CT prior to therapy; group 2 included 24 patients with pathologies other than Pca and median age 53.5 years (range: 18-80 years) who underwent Ga-68 PSMA PET/CT as part of another study in our department. Patients in group 2 did not have any prostate pathology and served as controls for the study. The segmented images of prostate (3-D image) were used to calculate 11 Haralick texture features in MATLAB. SUVmax was also evaluated. All parameters were compared among the two groups using appropriate statistical analysis and P value <0.05 was considered significant.

RESULTS

All 11 Haralick texture features, as well as SUVmax, were significantly different among Pca and controls (P < 0.05). Among the texture features, contrast was most significant (P value of Mann-Whitney U <0.001) in differentiating Pca from normal prostate with AUROC curve of 82.9% with sensitivity and specificity 83.30% and 73.30%, respectively at cut-off 0.640. SUVmax was also significant with AUROC curve 94.0% and sensitivity and specificity 62.5% and 90%, respectively at cut-off 5.7. A significant negative correlation of SUVmax was observed with contrast.

CONCLUSION

Haralick texture features have a significant role in differentiating Pca and normal prostate.

Clinical Applications of Artificial Intelligence in Positron Emission Tomography of Lung Cancer

The ability of a computer to perform tasks normally requiring human intelligence or artificial intelligence (AI) is not new. However, until recently, practical applications in medical imaging were limited, especially in the clinic. With advances in theory, microelectronic circuits, and computer architecture as well as our ability to acquire and access large amounts of data, AI is becoming increasingly ubiquitous in medical imaging. Of particular interest to our community, radiomics tries to identify imaging features of specific pathology that can represent, for example, the texture or shape of a region in the image. This is conducted based on a review of mathematical patterns and pattern combinations. The difficulty is often finding sufficient data to span the spectrum of disease heterogeneity because many features change with pathology as well as over time and, among other issues, data acquisition is expensive. Although we are currently in the early days of the practical application of AI to medical imaging, research is ongoing to integrate imaging, molecular pathobiology, genetic make-up, and clinical manifestations to classify patients into subgroups for the purpose of precision medicine, or in other words, predicting a priori treatment response and outcome. Lung cancer is a functionally and morphologically heterogeneous disease. Positron emission tomography (PET) is an imaging technique with an important role in the precision medicine of patients with lung cancer that helps predict early response to therapy and guides the selection of appropriate treatment. Although still in its infancy, early results suggest that the use of AI in PET of lung cancer has promise for the detection, segmentation, and characterization of disease as well as for outcome prediction.

Molecular Imaging in Primary Staging of Prostate Cancer Patients: Current Aspects and Future Trends

Accurate primary staging is the cornerstone in all malignancies. Different morphological imaging modalities are employed in the evaluation of prostate cancer (PCa). Regardless of all developments in imaging, invasive histopathologic evaluation is still the standard method for the detection and staging of the primary PCa. Magnetic resonance imaging (MRI) and computed tomography (CT) play crucial roles; however, functional imaging provides additional valuable information, and it is gaining ever-growing acceptance in the management of PCa. Targeted imaging with different radiotracers has remarkably evolved in the past two decades. [111In]In-capromab pendetide scintigraphy was a new approach in the management of PCa. Afterwards, positron emission tomography (PET) tracers such as [11C/18F]choline and [11C]acetate were developed. Nevertheless, none found a role in the primary staging. By introduction of the highly sensitive small molecule prostate-specific membrane antigen (PSMA) PET/CT, as well as recent developments in MRI and hybrid PET/MRI systems, non-invasive staging of PCa is being contemplated. Several studies investigated the role of these sophisticated modalities in the primary staging of PCa, showing promising results. Here, we recapitulate the role of targeted functional imaging. We briefly mention the most popular radiotracers, their diagnostic accuracy in the primary staging of PCa, and impact on patient management.

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) (P < 0.05); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) (P < 0.05) that was slightly higher for solid and subsolid nodules with a diameter of 5-10 mm (37 cases vs. 28 cases) (P < 0.05). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) (P < 0.05). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.

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.

Hypoxia in Lung Cancer Management: A Translational Approach

Simple Summary

Hypoxia is a common feature of lung cancers. Nonetheless, no guidelines have been established to integrate hypoxia-associated biomarkers in patient management. Here, we discuss the current knowledge and provide translational novel considerations regarding its clinical detection and targeting to improve the outcome of patients with non-small-cell lung carcinoma of all stages.

Abstract

Lung cancer represents the first cause of death by cancer worldwide and remains a challenging public health issue. Hypoxia, as a relevant biomarker, has raised high expectations for clinical practice. Here, we review clinical and pathological features related to hypoxic lung tumours. Secondly, we expound on the main current techniques to evaluate hypoxic status in NSCLC focusing on positive emission tomography. We present existing alternative experimental approaches such as the examination of circulating markers and highlight the interest in non-invasive markers. Finally, we evaluate the relevance of investigating hypoxia in lung cancer management as a companion biomarker at various lung cancer stages. Hypoxia could support the identification of patients with higher risks of NSCLC. Moreover, the presence of hypoxia in treated tumours could help clinicians predict a worse prognosis for patients with resected NSCLC and may help identify patients who would benefit potentially from adjuvant therapies. Globally, the large quantity of translational data incites experimental and clinical studies to implement the characterisation of hypoxia in clinical NSCLC management.

Prognostic Value of Baseline Radiomic Features of 18F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma

This study investigates whether baseline ¹⁸F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent ¹⁸F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinkage and selection operator regression were used to reduce the dimensionality within radiomic features. Cox proportional hazards model was used to determine the prognostic factors for progression-free survival (PFS) and overall survival (OS). A prognostic stratification model was built in the training cohort and validated in the validation cohort using Kaplan-Meier survival analysis. In the training cohort, run length non-uniformity (RLN), extracted from a gray level run length matrix (GLRLM), was independently associated with PFS (hazard ratio (HR) = 15.7, p = 0.007) and OS (HR = 8.64, p = 0.040). The International Prognostic Index was an independent prognostic factor for OS (HR = 2.63, p = 0.049). A prognostic stratification model was devised based on both risk factors, which allowed identification of three risk groups for PFS and OS in the training (p < 0.001 and p < 0.001) and validation (p < 0.001 and p = 0.020) cohorts. Our results indicate that the baseline ¹⁸F-FDG PET radiomic feature, RLN_GLRLM, is an independent prognostic factor for survival outcomes. Furthermore, we propose a prognostic stratification model that may enable tailored therapeutic strategies for patients with DLBCL.