» Articles » PMID: 20151423

Nuclear Grading of Primary Pulmonary Adenocarcinomas: Correlation Between Nuclear Size and Prognosis

Overview
Journal Cancer
Publisher Wiley
Specialty Oncology
Date 2010 Feb 13
PMID 20151423
Citations 25
Authors
Affiliations
Soon will be listed here.
Abstract

Background: According to the World Health Organization Classification of Tumors, the prognostic value of morphometric cytologic atypia has not been assessed in pulmonary adenocarcinoma.

Methods: Primary tumors of 133 pulmonary adenocarcinomas <or=2 cm were analyzed using an image processor for analytical pathology. The results were evaluated using receiver operator characteristic curve analysis, and survival curves were drawn by the Kaplan-Meier method. Furthermore, the results were applied to routine histological diagnosis. Four pathologists evaluated the nuclear factors relative to the size of small lymphocytes as a standard.

Results: By using the nuclear area and nuclear major axis dimension, lung adenocarcinomas were divisible into 2 groups showing extremely favorable prognosis and fairly favorable prognosis, without considering histological features or classification. A nuclear area level of <67 microm(2) was correlated with longer survival (P < .0001), and the 5-year survival rate was 90.4%. Similarly, a nuclear diameter level of <0.7 microm was correlated with longer survival (P = .0002), and the 5-year survival rate was 88.6%. The mean (+/-standard deviation [SD]) value of the kappa statistic for the 4 pathologists who evaluated the cases using the size of small lymphocytes as a standard was 0.58 +/- 0.10, and the mean (+/-SD) value of the accuracy metric was 0.66 +/- 0.10.

Conclusions: Nuclear area and nuclear major dimension are 2 useful independent markers for evaluating the prognosis of lung adenocarcinoma.

Citing Articles

Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach.

Bian C, Ashton G, Grant M, Rodriguez V, Martin I, Tsakiroglou A Cancers (Basel). 2024; 16(11).

PMID: 38893146 PMC: 11171264. DOI: 10.3390/cancers16112026.


Novel Insights Into the International Association for the Study of Lung Cancer Grading System for Lung Adenocarcinoma.

Tan K, Reiner A, Emoto K, Eguchi T, Takahashi Y, Aly R Mod Pathol. 2024; 37(7):100520.

PMID: 38777035 PMC: 11260232. DOI: 10.1016/j.modpat.2024.100520.


A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.

Kim P, Hwang H, Choi G, Sung H, Ahn B, Uh J Sci Rep. 2024; 14(1):6366.

PMID: 38493247 PMC: 10944489. DOI: 10.1038/s41598-024-56867-9.


The prognostic role of single cell invasion and nuclear diameter in early oral tongue squamous cell carcinoma.

Almangush A, Hagstrom J, Haglund C, Kowalski L, Coletta R, Makitie A BMC Cancer. 2024; 24(1):213.

PMID: 38360653 PMC: 10870554. DOI: 10.1186/s12885-024-11954-y.


Proposed novel grading system for stage I invasive lung adenocarcinoma and a comparison with the 2020 IASLC grading system.

Wang S, Li Y, Sun X, Dong J, Liu L, Liu J Thorac Cancer. 2024; 15(7):519-528.

PMID: 38273667 PMC: 10912529. DOI: 10.1111/1759-7714.15204.