Wolf in Sheep’s Clothing: Primary Lung Cancer Mimicking Benign Entities

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer

OBJECTIVE

Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features.

METHODS

This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS).

RESULTS

Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival.

CONCLUSION

Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early.

CRITICAL RELEVANCE STATEMENT

Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging.

KEY POINTS

Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.

Combined model of radiomics, clinical, and imaging features for differentiating focal pneumonia-like lung cancer from pulmonary inflammatory lesions: an exploratory study

Background

Only few studies have focused on differentiating focal pneumonia-like lung cancer (F-PLC) from focal pulmonary inflammatory lesion (F-PIL). This exploratory study aimed to evaluate the clinical value of a combined model incorporating computed tomography (CT)-based radiomics signatures, clinical factors, and CT morphological features for distinguishing F-PLC and F-PIL.

Methods

In total, 396 patients pathologically diagnosed with F-PLC and F-PIL from two medical institutions between January 2015 and May 2021 were retrospectively analyzed. Patients from center 1 were included in the training (n = 242) and internal validation (n = 104) cohorts. Moreover, patients from center 2 were classified under the external validation cohort (n = 50). The clinical and CT morphological characteristics of both groups were compared first. And then, a clinical model incorporating clinical and CT morphological features, a radiomics model reflecting the radiomics signature of lung lesions, and a combined model were developed and validated, respectively.

Results

Age, gender, smoking history, respiratory symptoms, air bronchogram, necrosis, and pleural attachment differed significantly between the F-PLC and F-PIL groups (all P < 0.05). For the clinical model, age, necrosis, and pleural attachment were the most effective factors to differentiate F-PIL from F-PLC, with the area under the curves (AUCs) of 0.838, 0.819, and 0.717 in the training and internal and external validation cohorts, respectively. For the radiomics model, five radiomics features were found to be significantly related to the identification of F-PLC and F-PIL (all P < 0.001), with the AUCs of 0.804, 0.877, and 0.734 in the training and internal and external validation cohorts, respectively. For the combined model, five radiomics features, age, necrosis, and pleural attachment were independent predictors for distinguishing between F-PLC and F-PIL, with the AUCs of 0.915, 0.899, and 0.805 in the training and internal and external validation cohorts, respectively. The combined model exhibited a better performance than had the clinical and radiomics models.

Conclusions

The combined model, which incorporates CT-based radiomics signatures, clinical factors, and CT morphological characteristics, is effective in differentiating F-PLC from F-PIL.

M2 macrophage-derived exosomal long non-coding RNA AGAP2-AS1 enhances radiotherapy immunity in lung cancer by reducing microRNA-296 and elevating NOTCH2

Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) play vital roles in human diseases. We aimed to identify the effect of the lncRNA AGAP2 antisense RNA 1 (AGAP2-AS1)/miR-296/notch homolog protein 2 (NOTCH2) axis on the progression and radioresistance of lung cancer. Expression of AGAP2-AS1, miR-296, and NOTCH2 in lung cancer cells and tissues from radiosensitive and radioresistant patients was determined, and the predictive role of AGAP2-AS1 in the prognosis of patients was identified. THP-1 cells were induced and exosomes were extracted, and the lung cancer cells were respectively treated with silenced AGAP2-AS1, exosomes, and exosomes upregulating AGAP2-AS1 or downregulating miR-296. The cells were radiated under different doses, and the biological processes of cells were assessed. Moreover, the natural killing cell-mediated cytotoxicity on lung cancer cells was determined. The relationships between AGAP2-AS1 and miR-296, and between miR-296 and NOTCH2 were verified. AGAP2-AS1 and NOTCH2 increased while miR-296 decreased in radioresistant patients and lung cancer cells. The malignant behaviors of radioresistant cells were promoted compared with the parent cells. Inhibited AGAP2-AS1, macrophage-derived exosomes, and exosomes overexpressing AGAP2-AS1 or inhibiting miR-296 facilitated the malignant phenotypes of radioresistant lung cancer cells. Furthermore, AGAP2-AS1 negatively regulated miR-296, and NOTCH2 was targeted by miR-296. M2 macrophage-derived exosomal AGAP2-AS1 enhances radiotherapy immunity in lung cancer by reducing miR-296 and elevating NOTCH2. This study may be helpful for the investigation of radiotherapy of lung cancer.

The radiologist's role in lung cancer screening

Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

M2 macrophages-derived exosomal microRNA-501-3p promotes the progression of lung cancer via targeting WD repeat domain 82

Background

Exosomes are known to transmit microRNAs (miRNAs) to affect cancer progression, while the role of M2 macrophages-derived exosomes (M2 exosomes) conveying miR-501-3p in lung cancer (LC) remains unknown. We aim to explore the role of exosomal miR-501-3p in LC development via targeting WD repeat domain 82 (WDR82).

Methods

Lung cancer tissue and normal tissue specimens were collected, in which tumor-associated macrophages (TAM) were measured by immunohistochemistry. M2 macrophages were induced and treated with altered miR-501-3p, and then the exosomes were extracted and identified. MiR-501-3p and WDR82 expression in LC tissues and cell liens was determined. The predictive role of miR-501-3p in prognosis of LC patients was assessed, and the proliferation, colony formation ability, invasion, migration and apoptosis of the LC cells were determined. Targeting relationship between miR-501-3p and WDR82 was confirmed.

Results

TAM level was elevated in lung cancer tissues. MiR-501-3p was upregulated while WDR82 was downregulated in LC tissues and cell lines, and the M2 exosomes further upregulated miR-501-3p. M2 exosomes and exosomal miR-501-3p promoted LC cell growth. MiR-501-3p inhibition reversed the effect of M2 exosomes on LC cells. WDR82 was confirmed as a target gene of miR-501-3p.

Conclusion

M2 macrophages-derived exosomal miR-501-3p promotes the progression of LC via downregulating WDR82.

Characterisation of microbiota in saliva, bronchoalveolar lavage fluid, non-malignant, peritumoural and tumour tissue in non-small cell lung cancer patients: a cross-sectional clinical trial

BACKGROUND

While well-characterised on its molecular base, non-small cell lung cancer (NSCLC) and its interaction with local microbiota remains scarcely explored. Moreover, current studies vary in source of lung microbiota, from bronchoalveolar lavage fluid (BAL) to tissue, introducing potentially differing results. Therefore, the objective of this study was to provide detailed characterisation of the oral and multi-source lung microbiota of direct interest in lung cancer research. Since lung tumours in lower lobes (LL) have been associated with decreased survival, characteristics of the microbiota in upper (UL) and lower tumour lobes have also been examined.

METHODS

Using 16S rRNA gene sequencing technology, we analysed microbiota in saliva, BAL (obtained directly on excised lobe), non-malignant, peritumoural and tumour tissue from 18 NSCLC patients eligible for surgical treatment. Detailed taxonomy, diversity and core members were provided for each microbiota, with analysis of differential abundance on all taxonomical levels (zero-inflated binomial general linear model with Benjamini-Hochberg correction), between samples and lobe locations.

RESULTS

Diversity and differential abundance analysis showed clear separation of oral and lung microbiota, but more importantly, of BAL and lung tissue microbiota. Phylum Proteobacteria dominated tissue samples, while Firmicutes was more abundant in BAL and saliva (with class Clostridia and Bacilli, respectively). However, all samples showed increased abundance of phylum Firmicutes in LL, with decrease in Proteobacteria. Also, clades Actinobacteria and Flavobacteriia showed inverse abundance between BAL and extratumoural tissues depending on the lobe location. While tumour microbiota seemed the least affected by location, peritumoural tissue showed the highest susceptibility with markedly increased similarity to BAL microbiota in UL. Differences between the three lung tissues were however very limited.

CONCLUSIONS

Our results confirm that BAL harbours unique lung microbiota and emphasise the importance of the sample choice for lung microbiota analysis. Further, limited differences between the tissues indicate that different local tumour-related factors, such as tumour type, stage or associated immunity, might be the ones responsible for microbiota-shaping effect. Finally, the "shift" towards Firmicutes in LL might be a sign of increased pathogenicity, as suggested in similar malignancies, and connected to worse prognosis of the LL tumours.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT03068663. Registered February 27, 2017.

Diagnostic and clinical features of lung cancer associated with cystic airspaces

"Lung cancer associated with cystic airspaces" is an uncommon manifestation, in which lung cancer presents on imaging studies with a cystic area with associated consolidation and/or ground glass. With the widespread use of computed tomography (CT), both in clinical practice and for lung cancer screening, these tumors are being more frequently recognized. An association of this entity with smoking has been established with the majority of cases reported being in former and current smokers. The true pathogenesis of the cystic airspace is not yet fully understood. Different causes of this cystic airspace have been described, including a check-valve mechanism obstructing the small airways, lepidic growth of adenocarcinoma on emphysematous lung parenchyma, cyst formation of tumor and tumor growth along the wall of a pre-existing bulla. Adenocarcinoma is the commonest histological type, followed by squamous cell carcinoma. Two classification systems have been described, based on morphological features of the lesion, taking into account both the cystic airspace as well as the morphology of the surrounding consolidation or ground glass. The cystic component may mislead radiologists to a benign etiology and the many different faces on imaging can make early diagnosis challenging. Special attention should be made to focal or diffuse wall thickening and consolidation or ground glass abutting or interspersed with cystic airspaces. Despite their atypical morphology, staging and management currently are still similar to that of other lung cancer types. Although the rarity of this entity will hamper larger studies, numerous aspects regarding this particular lung cancer type still need to be unraveled. This manuscript reviews the CT-imaging findings and gives an overview of available data in the English literature on pathogenesis, histopathology and clinical findings. Differential diagnosis and pitfalls are discussed as well as future directions regarding staging and management.

How long is too long? A scoping review of health system delays in lung cancer

Earlier access to lung cancer specialist (LCS) care improves survival, highlighting the need for streamlined patient referral. International guidelines recommend 14-day maximum time intervals from general practitioner (GP) referral to first LCS appointment (“GP–LCS interval”), and diagnosis to treatment (“treatment interval”). We compared time intervals in lung cancer care against timeframe benchmarks, and explored barriers and facilitators to timely care.

We conducted a scoping review of literature from MEDLINE, Embase, Scopus and hand searches. Primary end-points were GP–LCS and treatment intervals. Performance against guidelines and factors responsible for delays were explored. We used descriptive statistics and nonparametric Wilcoxon rank sum tests to compare intervals in studies reporting fast-track interventions.

Of 1343 identified studies, 128 full-text articles were eligible. Only 33 (26%) studies reported GP–LCS intervals, with an overall median of 7 days and distributions largely meeting guidelines. Overall, 52 (41%) studies reported treatment intervals, with a median of 27 days, and distributions of times falling short of guidelines. There was no effect of fast-track interventions on reducing time intervals. Lack of symptoms and multiple procedures or specialist visits were suggested causes for delay.

Although most patients with lung cancer see a specialist within a reasonable timeframe, treatment commencement is often delayed. There is regional variation in establishing timeliness of care.

Delays to lung cancer care occur, especially in secondary care; variation in timeframe guidelines needs addressinghttp://ow.ly/hZt730kvKAb