Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: Correlation with pathologic prognostic factors

Yanagawa, Masahiro; Tanaka, Yūko; Kusumoto, Masahiko; Watanabe, Shun‐ichi; Tsuchiya, Ryosuke; Honda, Osamu; Sumikawa, Hiromitsu; Inoue, Akira; Inoue, Masayoshi; Okumura, Meinoshin; Tomiyama, Noriyuki; Johkoh, Takeshi

doi:10.1016/j.lungcan.2010.03.009

Cited by 43 publications

(36 citation statements)

References 37 publications

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“…Thanks to the development of specific software, volumetric measurement of SSNs has become accurate over the years with a successful segmentation of up to 97% of the nodules [75,[78][79][80]. Similar results have been reported in the detection and segmentation of PSNs and, interestingly, a quantification of the solid component was related to pathological prognostic factors, such as lymphatic, vascular and pleural invasion [75,81,82].…”

Section: Factors Influencing Nodule Measurement Variationssupporting

confidence: 62%

Lung nodules: size still matters

et al. 2017

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The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

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Section: Factors Influencing Nodule Measurement Variationssupporting

confidence: 62%

Lung nodules: size still matters

et al. 2017

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“…Various decision algorithms and scores were first proposed based on CT intensities and human interpretation. 5,11,12,[14][15][16] In addition to the distribution of CT intensities, some studies also investigated the value of computerized texture analysis of the nodules (i.e., morphological properties) as an indicator of cancer aggressiveness. [17][18][19][20][21] The above-mentioned studies demonstrated the feasibility of predicting prognosis based on CT image features, where tumor texture was found to have an important role in characterizing cancer growth patterns.…”

Section: Introductionmentioning

confidence: 99%

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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“…Multiple logistic regression analysis revealed that percentage of solid volume of 63% or greater was an independent indicator associated with pleural invasion (P = .01). Multiple Cox proportional hazards regression analysis revealed that percentage of solid volume of 63% or greater was a significant indicator of lower disease-free survival (hazard ratio, 18 …”

Section: Materials and Methods:-mentioning

confidence: 99%