Prognostic value of tumor solid-part size and solid-part volume in patients with clinical stage I non-small cell lung cancer

Prognostic impact of artificial intelligence-based volumetric quantification of the solid part of the tumor in clinical stage 0-I adenocarcinoma

INTRODUCTION

The size of the solid part of a tumor, as measured using thin-section computed tomography, can help predict disease prognosis in patients with early-stage lung cancer. Although three-dimensional volumetric analysis may be more useful than two-dimensional evaluation, measuring the solid part of some lesions is difficult using this methods. We developed an artificial intelligence-based analysis software that can distinguish the solid and non-solid parts (ground-grass opacity). This software calculates the solid part volume in a totally automated and reproducible manner. The predictive performance of the artificial intelligence software was evaluated in terms of survival or recurrence-free survival.

METHODS

We analyzed the high-resolution computed tomography images of the primary lesion in 772 consecutive patients with clinical stage 0-I adenocarcinoma. We performed automated measurement of the solid part volume using an artificial intelligence-based algorithm in collaboration with FUJIFILM Corporation. The solid part size, the solid part volume based on traditional three-dimensional volumetric analysis, and the solid part volume based on artificial intelligence were compared.

RESULTS

Higher areas under the curve related to the solid part volume were provided by the artificial intelligence-based method (0.752) than by the solid part size (0.722) and traditional three-dimensional volumetric analysis-based method (0.723). Multivariate analysis demonstrated that the solid part volume based on artificial intelligence was independently correlated with overall survival (P = 0.019) and recurrence-free survival (P < 0.001).

CONCLUSION

The solid part volume measured by artificial intelligence was superior to conventional methods in predicting the prognosis of clinical stage 0-I adenocarcinoma.

The utility of three-dimensional computed tomography for prediction of tumor invasiveness in clinical stage IA lung adenocarcinoma

Background

It is critical to have an accurate measurement of solid tumor size in order to predict the invasiveness of small lung adenocarcinomas. Some lesions cannot be measured accurately via High-resolution computed tomography (HRCT) due to their irregular shape and unclear borders. For this reason, we evaluated the relative efficacy of three-dimensional (3D) CT for predicting invasive adenocarcinoma.

Methods

We evaluated 195 patients with clinical stage IA adenocarcinomas, including 109 with lesions documented as invasive that were surgically resected at our institute during 2017. All lesions were categorized as either (I) lesions that were difficult to evaluate (i.e., hazy lesions; HL) or (II) more typical lesions (TL). The relationships between solid tumor size as determined by HRCT, solid tumor volume as determined by 3D CT and pathologic diagnosis were evaluated.

Results

Fifty-seven patients (29%) were diagnosed with HL. We set the cut-off value for the solid volume at 225 mm³ as predictive for invasive adenocarcinoma. When evaluating all 195 patients as a group, the accuracy, sensitivity, and specificity based on the solid tumor volume were similar to those based on the solid tumor size. When we limit our analysis to the HL group, the specificity based on solid tumor volume (65.5%) was higher than that based on solid tumor size (44.8%) with a difference that approached statistical significance (P=0.070).

Conclusions

3D CT was equivalent to HRCT for predicting invasive adenocarcinoma and may be particularly useful for diagnosing lesions that are difficult to evaluate on HRCT.

Prognostic impact of solid tumor component diameter in early-stage non-small cell lung carcinoma treated with intensity-modulated fractionated radiotherapy: a retrospective analysis impact of solid tumor component diameter in NSCLC treated with IMRT

Objective:

To investigate the suitability of the new diameter-based subgroupings of the eighth edition Tumor Node Metastasis (TNM) classification system regarding radiotherapy treatment for early-stage non-small-cell lung cancer (NSCLC), we retrospectively re-analyzed the clinical data of patients treated with intensity-modulated radiotherapy using non-coplanar beams (ncIMRT) for Stage I NSCLC.

Methods:

Between March 2011 and March 2018, 92 patients with 94 tumors who were diagnosed with Stage I NSCLC according to the seventh edition TNM classification system were enrolled and underwent ncIMRT of 75 Gy in 30 fractions. Local control (LC), progression-free survival (PFS), and overall survival (OS) were retrospectively investigated according to the T-classification subdivisions of the eighth edition and maximal solid tumor component diameter.

Results:

The median follow-up period was 32.5 months. The median maximum tumor and solid tumor component diameters were 22 mm and 18 mm, respectively. 3-year LC, PFS, and OS rates were 84.1%, 69.4%, and 85.3%, respectively. The 3-year LC rates were 91.0 and 76.8% in the groups with tumor diameter ≤2 cm and >2 cm, corresponding to the T1c and T1b subdivisions of the eighth edition, respectively (p = 0.24). In the ≤2 cm and >2 cm solid tumor component groups, the 3 year LC rates were 93.6 and 63.2%, respectively, which were significantly different (p = 0.007).

Conclusion:

LC rates after radiotherapy in patients with Stage I NSCLC were correlated with solid tumor component diameter. High LC rates in patients with solid tumor components <2 cm in diameter were associated with high PFS and OS rates.

Advances in knowledge:

This study suggests that the eighth edition TNM classification system, which focuses on solid tumor components rather than tumor diameter, can be applied to radiotherapy.