Prognostic Importance of Volumetric Measurements in Stage I Lung Adenocarcinoma

Yanagawa, Masahiro; Tanaka, Yūko; Leung, Ann N.; Morii, Eiichi; Kusumoto, Masahiko; Watanabe, Shun‐ichi; Watanabe, Hirokazu; Inoue, Masayoshi; Okumura, Meinoshin; Gyobu, Tomoko; Ueda, Ken; Honda, Osamu; Sumikawa, Hiromitsu; Johkoh, Takeshi; Tomiyama, Noriyuki

doi:10.1148/radiol.14131903

Cited by 45 publications

(30 citation statements)

References 35 publications

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“…These slice thickness differences were thought to be one of the reasons for the discrepancies in the results. Further, other reports have shown that the degree of solid component on chest CT was more highly relevant to the pathological invasiveness and prognosis than the total tumor size [22][23][24], whereas our results showed that SUVmax was a more reliable predictive factor of visceral pleural invasion than the solid component size and ratio on HRCT. In fact, SUVmax contributed the most to the multivariate models, and the present study demonstrated that the diagnostic performance for visceral pleural Nonetheless, the present study has certain limitations.…”

Section: Discussioncontrasting

confidence: 82%

Predicting pleural invasion using HRCT and 18F-FDG PET/CT in lung adenocarcinoma with pleural contact

et al. 2015

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Section: Discussioncontrasting

confidence: 82%

Predicting pleural invasion using HRCT and 18F-FDG PET/CT in lung adenocarcinoma with pleural contact

et al. 2015

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“…The volume of the solid part of the lung carcinoma and the solid part ratio were reported as independent indicators of prognosis in stage I adenocarcinoma patients treated with surgery. 33 Though the method of volume measurement was different, these results are consistent with our study. Other authors reported a correlation between ATN and prognosis in patients with small pulmonary adenocarcinomas.…”

Section: Discussionsupporting

confidence: 91%

Prognostic Value of Semiautomatic CT Volumetry in Patients With Stage I Non–Small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy

Satoh

Nambu²,

Saito³

et al. 2016

Journal of Computer Assisted Tomography

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“…This demonstrates the importance of separating the computerized analyses of CT regions from GGO and solid components to assess adenocarcinoma cancer aggressiveness. 24 A majority of studies focused on the ratio of GGO and solid quantities, 5,11,12,[14][15][16] whereas our work provides a novel perspective on how to interpret imaging information from CT examinations by suggesting that most of the information related to adenocarcinoma aggressiveness is located in the solid components of the tumor.…”

Section: Discussionmentioning

confidence: 95%

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

et al. 2015

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Purpose: To investigate the importance of presurgical computed tomography (CT) intensity and texture information from ground-glass opacities (GGO) and solid nodule components for the prediction of adenocarcinoma recurrence. Methods: For this study, 101 patients with surgically resected stage I adenocarcinoma were selected. During the follow-up period, 17 patients had disease recurrence with six associated cancer-related deaths. GGO and solid tumor components were delineated on presurgical CT scans by a radiologist. Computational texture models of GGO and solid regions were built using linear combinations of steerable Riesz wavelets learned with linear support vector machines (SVMs). Unlike other traditional texture attributes, the proposed texture models are designed to encode local image scales and directions that are specific to GGO and solid tissue. The responses of the locally steered models were used as texture attributes and compared to the responses of unaligned Riesz wavelets. The texture attributes were combined with CT intensities to predict tumor recurrence and patient hazard according to disease-free survival (DFS) time. Two families of predictive models were compared: LASSO and SVMs, and their survival counterparts: Cox-LASSO and survival SVMs.Results: The best-performing predictive model of patient hazard was associated with a concordance index (C-index) of 0.81 ± 0.02 and was based on the combination of the steered models and CT intensities with survival SVMs. The same feature group and the LASSO model yielded the highest area under the receiver operating characteristic curve (AUC) of 0.8 ± 0.01 for predicting tumor recurrence, although no statistically significant difference was found when compared to using intensity features solely. For all models, the performance was found to be significantly higher when image attributes were based on the solid components solely versus using the entire tumors (p < 3.08 × 10 −5 ). Conclusions: This study constitutes a novel perspective on how to interpret imaging information from CT examinations by suggesting that most of the information related to adenocarcinoma aggressiveness is related to the intensity and morphological properties of solid components of the tumor. The prediction of adenocarcinoma relapse was found to have low specificity but very high sensitivity. Our results could be useful in clinical practice to identify patients for which no recurrence is expected with a very high confidence using a presurgical CT scan only. It also provided an accurate estimation of the risk of recurrence after a given duration t from surgical resection (i.e., C-index = 0.81 ± 0.02). C 2015 American Association of Physicists in Medicine. [http://dx

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Prognostic Importance of Volumetric Measurements in Stage I Lung Adenocarcinoma

Abstract: Two volumetric measurements (solid volume, ≥1.5 cm(3); percentage of solid volume, ≥63%) were found to be independent indicators associated with increased likelihood of recurrence and/or death in patients with stage I adenocarcinoma.

Cited by 45 publications

References 35 publications

Predicting pleural invasion using HRCT and 18F-FDG PET/CT in lung adenocarcinoma with pleural contact

Predicting pleural invasion using HRCT and 18F-FDG PET/CT in lung adenocarcinoma with pleural contact

Prognostic Value of Semiautomatic CT Volumetry in Patients With Stage I Non–Small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy

Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

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