Volume doubling time of lung adenocarcinomas considering epidermal growth factor receptor mutation status of exon 19 and 21: three-dimensional volumetric evaluation

Abstract

Background: The current study was to investigate the volume doubling time (VDT) of lung adenocarcinomas considering epidermal growth factor receptor (EGFR) mutation status of exon 19 and 21, when compared with EGFR wide type.Methods: Eighty-eight patients with pathologically proven adenocarcinomas, which underwent two or more computed tomography (CT) scans spared by 25 or more days, were included. EGFR mutations at exons 19 and 21 were determined using amplification refractory mutation system and all patients were divided into three groups-EGFR wide type group, EGFR mutation in exon 19 and 21 groups. Threedimensional manual segmentations for all tumors were performed on first and latest follow-up CT scans; subsequently, VDTs were calculated and compared among three groups. Clinicopathoradiologic characters were also collected for subgroup analysis.Results: EGFR mutations occurred in 49 (55.7%) patients, 19 in exon 19 and 30 in exon 21, respectively.The median VDT of all patients (33 men, 55 women; median age, 62 years) was 214 days (range, −4,092 to 10,920 days). Highly differentiated adenocarcinomas (median, 408 days) demonstrated longer VDT than those moderately (median, 172 days) or poorly (median, 144 days) differentiated (P=0.04). The VDT distribution was similar among EGFR wide type group (median, 207 days), EGFR mutation in exon 19 group (median, 288 days) and exon 21 group (median, 144 days) (P=0.21). In subgroup analysis, the median VDT of adenocarcinomas with EGFR mutation in exon 19 was longer than that of EGFR wide type for males (P=0.03) or patients without spiculation sign in chest CT (P=0.04). Totally 24 adenocarcinomas presented negative VDTs, most of which tended to be stable. Positive VDT values were used for all median description.Conclusions: Overall VDT of lung adenocarcinomas seems not affected by EGFR mutation status.Researches with large population are warranted for further study.

Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome

“…An assumption that leaves for improvement is that primary tumor growth parameters were determined from meta-analyses values using only the tumor pathology 60 . We need further investigation to understand the influence of molecular biology, especially EGFR mutations 77,78 or ALK translocation because these patients have more multiple brain relapses but have better prognosis 79 .…”

“…An assumption that leaves for improvement is that primary tumor growth parameters were determined from meta-analyses values using only the tumor pathology 60 . We need further investigation to understand the influence of molecular biology, especially EGFR mutations 77,78 or ALK translocation because these patients have more multiple brain relapses but have better prognosis 79 . Moreover we need to understand the impact of treatments on the primary tumor, particularly with the advent of neoadjuvant and adjuvant treatments, which could reduce micrometastatic disease and thus risk of relapse 80–82 .Moreover, our model could be extended to account for more complex processes of the metastatic cascade, such as dormancy 83 and the possibility that the primary tumor is not the only one that can spread new lesions 84 .…”

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

“…An assumption that leaves for improvement is that primary tumor growth parameters were determined from meta-analyses values using only the tumor pathology 60 . We need further investigations to understand the influence of molecular biology, especially EGFR mutations 79,80 or ALK rearrangements because these patients have more multiple brain recurrences but have better prognosis 81 . Moreover we need to understand the impact of treatments on the primary tumor, particularly with the advent of neoadjuvant and adjuvant treatments, which could reduce micrometastatic disease and thus the risk of relapse [82][83][84] .Moreover, our model could be extended to account for more complex processes of the metastatic cascade, such as dormancy 85 and the possibility that the primary tumor is not the only one that can spread new lesions 86 .…”

Lung Cancer Radiotherapy: Simulation and Analysis Based on a Multicomponent Mathematical Model

“…In 2013, Benzekry et al recorded a serial of comprehensive experiments for several classical mathematical models for tumor growth [ 29 ]; in their paper, the parameters in many mathematical models include LM and GM for lung and breast cancer are been fitted with the experimental data. As a key reference, their lung data was extracted for estimating and fitting the parameters of our tumor growth model; also, we refined our model parameters according to the dada of the volume double time of lung cancer in some studies [ 30 , 31 ].…”