2021
DOI: 10.1155/2021/9999968
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Systematic Pan-Cancer Population-Based Analysis Reveals the Incidence and Prognosis of Lung Metastases at Diagnosis

Abstract: Background. Metastasis is one of the most prevalent causes of death in cancer patients and the lungs are among the organs most commonly affected by metastasis. However, analysis of the incidence and prognosis of lung metastasis (LM) based on primary cancer sites is lacking. Methods. We enrolled cancer patients with LM from the Surveillance, Epidemiology, and End Results (SEER) database. The risk factors for LM were determined using multivariate logistics regression. Forest plots were used to compare the impact… Show more

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Cited by 5 publications
(7 citation statements)
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“…Lymph node metastasis is the most common type of metastasis (17). Distant organ metastasis is rare (18). Early and accurate diagnosis is particularly important to establish the best treatment plan and prognosis.…”
Section: Discussionmentioning
confidence: 99%
“…Lymph node metastasis is the most common type of metastasis (17). Distant organ metastasis is rare (18). Early and accurate diagnosis is particularly important to establish the best treatment plan and prognosis.…”
Section: Discussionmentioning
confidence: 99%
“…Previous efforts to dissect PDAC tumors into subtypes used unbiased approaches with gene expression, proteomics, or metabolomics data to describe mutually exclusive subsets and defining signatures 25,26,28,29,31,33 . We approached the problem differently by classifying tumors based on a well-known clinical outcome associated with metastatic organotropism; specifically, longer-term survival among patients with lung-avid, liver-averse disease and poor outcomes with liver-avid disease 1416,63 . We generated two new large datasets obtained from only patients with proven adenocarcinoma in the pancreas or Ampulla of Vater tumors that were histologically pancreaticobiliary: 1) a 290 specimen RNA-seq dataset with genomic alterations from tumor-enriched regions from primary and metastatic tumors and 2) TCRB sequencing of 289 blood and 175 matched tumor samples, mostly overlapping with the RNA-seq dataset.…”
Section: Discussionmentioning
confidence: 99%
“…Previous efforts to dissect PDAC tumors into subtypes used unbiased approaches with gene expression, proteomics, or metabolomics data to describe mutually exclusive subsets and defining signatures 25,26,28,29,31,33 . We approached the problem differently by classifying tumors based on a wellknown clinical outcome associated with metastatic organotropism; specifically, longer-term survival among patients with lung-avid, liver-averse disease and poor outcomes with liver-avid disease [14][15][16]63 .…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…LPR was calculated for each patient by arranging all metastases in a rising order of diameters and grouping all metastases with similar diameter (with diameter difference ≤ 1 mm on axial chest CT) into a single cluster. The LPR was then calculated with the aid of a computer code (available in supplementary material S2) using the formula: LPR = (∑ c − ∑ i )/(∑ c + ∑ i ) where “c” is the sum number of metastases that can be clustered metastases and “i” the sum number of isolated metastases (Liang et al 2021 ). LPR ≤ 0 suggests dominance of the parallel spreading pathway and LPR > 0 suggests dominance of the linear pathway.…”
Section: Methodsmentioning
confidence: 99%