A Novel Strategy for the Diagnosis of Pulmonary High-Grade Neuroendocrine Tumor

Effect of age on human ACE2 and ACE2-expressing alveolar type II cells levels

BACKGROUND

Angiotensin-converting enzyme 2 (ACE2) is the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes COVID-19. Viral entry requires ACE2 and transmembrane protease serine 2 (TMPRSS2). Transcriptomic studies showed that children display lower ACE2 than adults, though gene expression levels do not always correlate with protein levels. We investigated the effect of age on ACE2 and TMPRSS2 protein expression in alveolar type II (AT2) cells in the lungs of children compared to adults. We also analysed the ratio of Ang-(1-7) to Ang II as a surrogate marker of ACE2 activity in the subjects' lung parenchyma.

METHODS

Ang II and Ang-(1-7) levels and ACE2 and TMPRSS2 protein expression were measured by radioimmunoassay and immunohistochemistry, respectively.

RESULTS

The amount of ACE2-expressing AT2 cells and ACE2 protein content were lower in children than in adults. Ang II levels were higher in children compared to adults and inversely correlated with the amount of ACE2-expressing AT2 cells. Children presented lower Ang-(1-7)/Ang II ratio than adult suggesting lower ACE2 activity in children. TMPRSS2 protein expression was not influenced by age.

CONCLUSIONS

These results expand on previous transcriptomic studies and may partially explain the low susceptibility of children to SARS-CoV-2 infection.

CATEGORY OF STUDY

Clinical original research IMPACT: Children display lower ACE2 protein content and activity compared to adults. Ang II levels were higher in children compared to adults and inversely correlated with the amount of ACE2-expressing AT2 cells TMPRSS2 protein expression was not influenced by age. These results expand on previous transcriptomic studies and may partially explain the low susceptibility of children to SARS-CoV-2 infection.

Cytological characteristics of histological types of lung cancer by cytomorphometric and flow cytometric analyses using liquid-based cytology materials

BACKGROUND

Distinguishing the histological types of lung cancer is essential for determining treatment strategies in clinical practice. In this study, cytomorphological characteristics and proliferative activities were compared among histological types of lung cancer by cytomorphometric and flow cytometric analyses using liquid-based cytology (LBC) samples.

METHODS

Scraped LBC samples from 73 surgically resected specimens were collected between August 2018 and November 2019. Papanicolaou-stained and paired Ki-67-stained slides were used for cytomorphometric analyses. Another sample for each case was analyzed using a flow cytometric system (LC-1000). The cell proliferation index (CPIx) was calculated to evaluate proliferative activity.

RESULTS

In total, 73 cases, including cases of adenocarcinoma (n = 53), squamous cell carcinoma (n = 14), small cell carcinoma (n = 1), large cell neuroendocrine carcinoma (NEC; n = 3), and pleomorphic carcinoma (n = 2) were evaluated. Small cell carcinoma and large cell NEC were categorized into a single group, NEC. The adenocarcinoma group tended to have a larger nuclear area and longer perimeter than other histological types. The NEC group had a considerably higher Ki-67 labeling index and significantly higher CPIx than other histological types (p = .030). A significant positive correlation was observed between the Ki-67 labeling index and CPIx for all cases (r = 0.362, p = .002).

CONCLUSION

The Ki-67 labeling index and flow cytometric analyses focus on proliferative activity for the distinction of histological types of lung cancer, thereby guiding clinical decision-making.

Diagnostic Efficacy of CT Examination on Early Detection of Lung Cancer during Pandemic of COVID-19

Background: Since the outbreak of COVID-19 in 2020, routine CT examination was recommended to hospitalized patients at some hospitals and discovered lung cancer patients at an early stage. This study aimed to investigate the detection efficacy of routine CT examination on early diagnosis of lung cancer, especially on pathological characteristics. Methods: The epidemic of COVID-19 outbreak in January 2020 in China, and routine CT examination was recommended to hospitalized patients in June 2020 and ended in July 2021. Based on the time points, we compared the diagnosis efficacy between three periods: pre-period, peri-period, and the period of routine CT examination. Results: During the period of routine CT examination, more early stages of lung cancer were detected and the tumor size was reduced to 2.14 cm from 3.21 cm at pre-period (p = 0.03). The proportion of lung adenocarcinoma and early stage adenocarcinoma was increased by 12% and 30% in the period of routine CT examination, with referral to the pre-period of CT examination (p < 0.05). A total of 61% of diagnosed patients had the wild type of TP53 gene during the period of routine CT examination, compared to 45% of patients at the pre-period of CT examination (p = 0.001). The median Ki-67 index was 15% among patients diagnosed at the period of routine CT examination and increased to 35% at the pre-period of CT examination (p < 0.001). The period of routine CT examination was associated with a 78% higher probability of detecting an early stage of adenocarcinoma (OR = 1.78, 95%CI 1.03, 3.08) but no significant association was observed for squamous cell carcinoma. From the pre-period to the period of routine CT examination, the proportion of female patients and non-smoking patients increased by 57% and 44%, respectively (p < 0.001). Conclusion: Routine CT examination could detect more lung cancer at an early stage, especially for adenocarcinoma, and detect patients with less aggressive features. Further studies were warranted to confirm the findings.

Deep Learning Facilitates Distinguishing Histologic Subtypes of Pulmonary Neuroendocrine Tumors on Digital Whole-Slide Images

Simple Summary

Challenges persist in diagnosing pulmonary neuroendocrine tumors. Our case study shows that deep learning combined with convolutional neural networks has the potential to assist in the diagnosis of pulmonary neuroendocrine tumors from digital whole-slide images.

Abstract

The histological distinction of lung neuroendocrine carcinoma, including small cell lung carcinoma (SCLC), large cell neuroendocrine carcinoma (LCNEC) and atypical carcinoid (AC), can be challenging in some cases, while bearing prognostic and therapeutic significance. To assist pathologists with the differentiation of histologic subtyping, we applied a deep learning classifier equipped with a convolutional neural network (CNN) to recognize lung neuroendocrine neoplasms. Slides of primary lung SCLC, LCNEC and AC were obtained from the Laboratory of Clinical and Experimental Pathology (University Hospital Nice, France). Three thoracic pathologists blindly established gold standard diagnoses. The HALO-AI module (Indica Labs, UK) trained with 18,752 image tiles extracted from 60 slides (SCLC = 20, LCNEC = 20, AC = 20 cases) was then tested on 90 slides (SCLC = 26, LCNEC = 22, AC = 13 and combined SCLC with LCNEC = 4 cases; NSCLC = 25 cases) by F1-score and accuracy. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The tumor maps were false colored and displayed side by side to original hematoxylin and eosin slides with superimposed pathologist annotations. The trained HALO-AI yielded a mean F1-score of 0.99 (95% CI, 0.939–0.999) on the testing set. Our CNN model, providing further larger validation, has the potential to work side by side with the pathologist to accurately differentiate between the different lung neuroendocrine carcinoma in challenging cases.