Morphologic and molecular classification of lung neuroendocrine neoplasms

Metović, Jasna; Barella, Marco; Bianchi, Fabrizio; Hofman, Paul; Hofman, Véronique; Remmelink, Myriam; Kern, Izidor; Carvalho, Lina; Pattini, Linda; Sonzogni, Angelica; Veronesi, Giulia; Harari, Sergio; Forest, Fabien; Papotti, Mauro; Pelosi, Giuseppe

doi:10.1007/s00428-020-03015-z

Cited by 55 publications

(49 citation statements)

References 82 publications

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“…Prior studies in the context of pulmonary NECs have suggested several biological differences between pulmonary LC and SC NECs. These differences have been shown to impact prognosis as well as treatment responsiveness 11,12 . Whether these biological traits carry the same weight among patients with digestive SC versus LC NECs is unclear and this is an area of unmet need in GEP‐NEN research.…”

Section: Discussionmentioning

confidence: 99%

Outcomes of small‐cell versus large‐cell gastroenteropancreatic neuroendocrine carcinomas: A population‐based study

Abdel‐Rahman

Fazio

2021

J Neuroendocrinology

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Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) represent a diverse group of malignancies with variable aetiology, histology, biology and treatment sensitivities. 1,2 Pathological classification of GEP-NENs and criteria identifying different subtypes of well-versus poorly differentiated (WD, PD) disease have evolved. The World Health Organization (WHO) 2000 classification system for GEP-NENs identified originally four categories (including WD neuroendocrine tumor [NET], WD neuroendocrine carcinoma [NEC], PD NEC and mixed neuroendocrine-exocrine carcinoma). 3Subsequently, the WHO 2010 classification adopted a proliferationmarker-based classification categorising GEP-NENs into G1, G2 and G3 according to Ki-67 and mitotic index in addition to mixed adenoneuroendocrine carcinoma. 4 More recently, the WHO 2019 classification not only introduced the concept of WD grade 3 tumors, but also classified patients with PD GEP-NENs (GEP-NECs) into small cell (SC) and large cell (LC) NECs. Additionally, it coined the term MiNEN (mixed neuroendocrine-non-neuroendocrine neoplasms). 5 Patients with PD GEP-NENs generally have very poor outcomes, and they are classified, in accordance with the most recent WHO classification, into LC and SC GEP-NECs. 6,7 Although multiple

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Section: Discussionmentioning

confidence: 99%

Outcomes of small‐cell versus large‐cell gastroenteropancreatic neuroendocrine carcinomas: A population‐based study

Abdel‐Rahman

Fazio

2021

J Neuroendocrinology

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“…Another interesting marker is insulinoma-associated protein 1 (INSM1), which reflects true neuroendocrine differentiation [ 31 ]. For future studies, it would be of interest, whether these histopathological parameters could also be reflected by quantitative imaging analysis, including the biological heterogeneity of pNET [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations with Tumor Grading and Proliferation

Meyer

Leonhardi

Höhn

et al. 2021

JCM

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Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, “S(1,1)SumEntrp” was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed “S(4,4)InvDfMom” (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. “Sumentrp” might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.

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“…Lung cancer starts in the lungs and progresses to the brain. Despite this, the lung cancer detection procedure is very ineffective because doctors will only be able to detect the illness after it has progressed to an advanced stage (Metovic, et al 2021, Abdullah and Abdulazeez, 2021, Malika, and Jaina, Yuvaraj, et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Improved Flower Pollination Algorithm for the Detection of Lung Cancer Detection in Humans Text Based Mining

Sungheetha¹

2021

Biosc Biotech Res Comm

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Lung cancer is a worldwide threat to humanity since its cells grow uncontrollably inside lungs leading to increased mortality rate. The lung cancer often poses serious breathing issues and it is been contributed majorly by smoking and inhaling smoke. Even with high medical advancements, the effective treatment and curing of lung cancer are not effective till date. Proper precautions and earlier stage detection may reduce the cancer to spread the entire organ. It is hence necessary to check with least minimal data i.e. text data can provide even a greater effectiveness in diagnosing the lung condition. Meta-heuristic algorithm influences greatly with its computational capability and offers stronger prediction of lung cancer at earlier stage with accurate analysis. In this paper, we develop a classification system using flower pollination algorithm (FPA) that tends to classify the medical text documents. The FPA algorithm classifies the medical text documents to diagnose the lung cancer in humans. The FPA is applied as an intelligent algorithm that imitates the behavior of pollination in flowering plants to identify the essential classes of lung cancer. It finds the relationship between the pollens to identify the essential classes based on flower position. The simulation is conducted to validate the effectiveness of the model with other meta-heuristic optimization methods that include bee colony optimization, and ant colony optimization algorithm. The results of simulation show that the proposed method undergoes effective classes of lung cancer than other existing methods that includes accuracy, sensitivity, specificity, f-measure and mean average percentage error.

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Morphologic and molecular classification of lung neuroendocrine neoplasms

Cited by 55 publications

References 82 publications

Outcomes of small‐cell versus large‐cell gastroenteropancreatic neuroendocrine carcinomas: A population‐based study

Outcomes of small‐cell versus large‐cell gastroenteropancreatic neuroendocrine carcinomas: A population‐based study

CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations with Tumor Grading and Proliferation

Improved Flower Pollination Algorithm for the Detection of Lung Cancer Detection in Humans Text Based Mining

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