Recent advances and current controversies in lung neuroendocrine neoplasms✰

Study on temperature change and nursing intervention of patients undergoing thoracoscopic surgery in lung tumor treatment

To investigate the changes of body temperature and nursing intervention of patients undergoing thoracoscopic surgery in lung tumor treatment. A total of 100 patients with lung cancer admitted to our hospital from January 2021 to January 2023 were selected as research objects, and were divided into control group and study group, with 50 cases in each group according to different treatment methods. All patients received thoracoscopic surgery, the control group received routine nursing, and the research group received perioperative nursing intervention on the basis of the above, and observed and compared the temperature changes, perioperative complications, postoperative rehabilitation, stress indexes and pain degree of the 2 groups of patients. The temperature at T1, T2, T3, and T4 was lower than that at T0, and the temperature at T3 was the lowest. The temperature at T1, T2, T3, and T4 in the study group was significantly higher than that in the control group (P < .05). The incidence of perioperative hypothermia, hypoxemia, chills, and agitation in study group was significantly lower than that in control group (P < .05). The anal exhaust time, consciousness recovery time, tracheal extubation time, operating room time, and hospital stay time in the study group were significantly shorter than those in the control group (P < .05). After operation, the levels of AD, NA, and Cor in both groups were higher than those before operation, and the levels of AD, NA, and Cor in the study group were significantly lower than those in the control group (P < .05). The visual analogue scale scores of the 2 groups at 12 hours and 48 hours after operation were lower than those of the control group at 6 hours, 12 hours, and 48 hours after operation, and the visual analogue scale scores of the study group at 6 hours, 12 hours, and 48 hours after operation were significantly lower than those of the control group (P < .05). Nursing intervention during thoracoscopic surgery for lung tumors can stabilize intraoperative and postoperative temperature changes, alleviate stress reaction and pain, reduce the risk of intraoperative hypothermia and hypoxemia, promote the smooth progress of surgery and improve prognosis.

Head-to-head: Should Ki67 proliferation index be included in the formal classification of pulmonary neuroendocrine neoplasms?

The reporting of lung neuroendocrine neoplasms (NENs) according to the 2021 World Health Organisation (WHO) is based on mitotic count per 2 mm2, necrosis assessment and a constellation of cytological and immunohistochemical details. Accordingly, typical carcinoid and atypical carcinoid are low- to intermediate-grade neuroendocrine tumours (NETs), while large-cell neuroendocrine carcinoma (NEC) and small-cell lung carcinoma are high-grade NECs. In small-sized diagnostic material (cytology and biopsy), the noncommittal term of carcinoid tumour/NET not otherwise specified (NOS) and metastatic carcinoid NOS have been introduced with regard to primary and metastatic diagnostic settings, respectively. Ki-67 antigen, a well-known marker of cell proliferation, has been included in the WHO classification as a non-essential but desirable criterion, especially to distinguish NETs from high-grade NECs and to delineate the provisional category of carcinoid tumours/NETs with elevated mitotic counts (> 10 mitoses per mm2) and/or Ki-67 proliferation index (≥ 30%). However, a wider use of this marker in the spectrum of lung NENs continues to be highly reported and debated, thus witnessing a never-subsided attention. Therefore, the arguments for and against incorporating Ki-67 in the classification and clinical practice of these neoplasms are discussed herein in detail.

An in-silico analysis reveals further evidence of an aggressive subset of lung carcinoids sharing molecular features of high-grade neuroendocrine neoplasms

Little is known as to whether there may be any pathogenetic link between pulmonary carcinoids and neuroendocrine carcinomas (NECs). A gene signature we previously found to cluster pulmonary carcinoids, large cell neuroendocrine carcinoma (LCNEC) and small cell lung carcinoma (SCLC), and which encompassed MEN1, MYC, MYCL1, RICTOR, RB1, SDHA, SRC and TP53 mutations or copy number variations (CNVs), was used to reclassify an independent cohort of 54 neuroendocrine neoplasms (NENs) [31 typical carcinoids (TC), 11 atypical carcinoids (AC) and 12 SCLC], by means of transcriptome and mutation data. Unsupervised clustering analysis identified two histology-independent clusters, namely CL1 and CL2, where 17/42 (40.5%) carcinoids and all the SCLC samples fell into the latter. CL2 carcinoids affected survival adversely, were enriched in T to G transversions or T > C/C > T transitions in the context of specific mutational signatures, presented with at least 1.5-fold change (FC) increase of gene mutations including TSC2, SMARCA2, SMARCA4, ERBB4 and PTPRZ1, differed for gene expression and showed epigenetic changes in charge of MYC and MTORC1 pathways, cellular senescence, inflammation, high-plasticity cell state and immune system exhaustion. Similar results were also found in two other independent validation sets comprising 101 lung NENs (24 carcinoids, 21 SCLC and 56 LCNEC) and 30 carcinoids, respectively. We herein confirmed an unexpected sharing of molecular traits along the spectrum of lung NENs, with a subset of genomically distinct aggressive carcinoids sharing molecular features of high-grade neuroendocrine neoplasms.

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.

Small-Cell Carcinoma of the Lung: What We Learned about It?

Small-cell lung carcinoma (SCLC) is a high-grade aggressive disease that belongs to the neuroendocrine (NE) group of lung tumors that also includes typical carcinoid, atypical carcinoid, and large-cell NE carcinoma. SCLC has specific histological diagnostic criteria that are sometimes troublesome to be assessed in cytological samples that indeed represent the most frequent source of diagnostic material due to the typical advanced presentation at the onset of SCLC. However, cytological preparations could be in some instances more reliable than histology due to the better preservation of nuclear details. Cytological criteria for diagnosis of SCLC include high cellularity, small cell size, scant cytoplasm, coarsely granulated chromatin with "salt-and-pepper" appearance, inconspicuous or absent nucleoli, Azzopardi crush effect, and necrotic debris in the background. Despite being distinctive, these features could be incomplete to differentiate SCLC with other small-cell neoplasia. Therefore, immunocytochemical determination of diagnostic biomarkers is crucial to achieve a confident diagnosis. Furthermore, recent findings on molecular and transcriptomic studies of SCLC revealed the potential rise of new predictive and prognostic biomarkers that, whenever validated by immunocytochemistry, may potentially assist to tailor the best therapy, including immune checkpoint inhibition.