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

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

Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

Discrepancy Between Radiological and Pathological Tumor Size in Early-Stage Non-Small Cell Lung Cancer: A Multicenter Study

Discrepancies between radiological whole tumor size (RTS) and pathological whole tumor size (PTS) are sometimes observed. Unexpected pathological upsize may lead to insufficient margins during procedures like sub lobar resections. Therefore, this study aimed to investigate the current status of these discrepancies and identify factors resulting in pathological upsize in patients with early-stage non-small cell lung cancer (NSCLC). Data from a multicenter database of 3092 patients with clinical stage 0-IA NSCLC who underwent pulmonary resection were retrospectively analyzed. Differences between the RTS and PTS were evaluated using Pearson's correlation analysis and Bland-Altman plots. Unexpected pathological upsize was defined as an upsize of ≥1 cm when compared to the RTS, and the predictive factors of this upsize were identified based on multivariable analyses. The RTS and PTS showed a positive linear relationship (r = 0.659), and the RTS slightly overestimated the PTS. The Bland-Altman plot showed 131 of 3092 (5.2%) cases were over the upper 95% limits of agreement. In multivariable analyses, a maximum standardized uptake value (SUVmax) of the primary tumor on 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography (odds ratio [OR], 1.070; 95% confidence interval [CI], 1.035-1.107; P < 0.001) and the adenocarcinoma histology (OR, 1.899; 95% CI, 1.071-3.369; P =0.049) were independent predictors of unexpected pathological upsize. More of the adenocarcinomas with pathological upsize were moderately or poorly differentiated, when compared to those without. The RTS tends to overestimate the PTS; however, care needs to be taken regarding unexpected pathological upsize, especially in adenocarcinomas with a high SUVmax.

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

Background

Lung cancer has been one of the most deadly illnesses all over the world, and radiotherapy can be an effective approach for treating lung cancer. Now, mathematical model has been extended to many biomedical fields to give a hand for analysis, evaluation, prediction, and optimization.

Methods

In this paper, we propose a multicomponent mathematical model for simulating the lung cancer growth as well as radiotherapy treatment for lung cancer. The model is digitalized and coded for computer simulation, and the model parameters are fitted with many research and clinical data to provide accordant results along with the growth of lung cancer cells in vitro.

Results

Some typical radiotherapy plans such as stereotactic body radiotherapy, conventional fractional radiotherapy, and accelerated hypofractionated radiotherapy are simulated, analyzed, and discussed. The results show that our mathematical model can perform the basic work for analysis and evaluation of the radiotherapy plan.

Conclusion

It will be expected that in the near future, mathematical model will be a valuable tool for optimization in personalized medical treatment.

Prognostic impact of solid-part tumour volume doubling time in patients with radiological part-solid or solid lung cancer

OBJECTIVES

The measurement of part-solid and whole tumour sizes in patients with non-small-cell lung cancer (NSCLC) using computed tomography (CT) has been widely accepted for assessing clinical outcomes. Although the volume doubling time (VDT) of a tumour is useful for distinguishing high-risk nodules from low-risk ones, it remains to be clarified whether separate calculation of whole-tumour VDT and solid-part tumour VDT (SVDT) greatly affects the survival rate of patients with radiologically node-negative part-solid or solid NSCLC.

METHODS

The study included 258 patients with NSCLC who had radiologically node-negative, part-solid or solid tumours and who had at least 2 preoperative CT scans taken more than 30 days apart followed by radical lobectomy and systemic lymph node dissection between January 2012 and December 2015. Univariable and multivariable analyses of recurrence-free survival were performed using the Cox proportional hazards regression model.

RESULTS

The mean whole-tumour VDT and SVDT were 375 and 458 days, respectively. Multivariable analyses demonstrated that whole-tumour VDT (P = 0.003), SVDT (P < 0.001), solid-part tumour size, whole-tumour size and comorbidities significantly affected the recurrence-free survival. Using the receiver operating characteristic curve, the cut-off value of the SVDT for recurrence was 215 days, and the 5-year recurrence-free survival rates for patients with SVDT >215 days and those with SVDT <215 days were 85.7% and 43.0%, respectively (P < 0.001).

CONCLUSION

The calculation of SVDT in patients with node-negative, part-solid or solid NSCLC is highly useful for predicting postoperative survival outcomes.