Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

Huang, Yanqi; Liang, Changhong; He, Lan; Tian, Jie; Liang, Chun-Ling; Chen, Xin; Ma, Zhanjun; Liu, Zaiyi

doi:10.1200/jco.2015.65.9128

Cited by 1,396 publications

(1,042 citation statements)

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“…Variations in acquisition and image reconstruction parameters can introduce changes that are not due to underlying biologic effects (17). Recently, CT has been the most widely used imaging modality, in consideration of its standard-of-care images (41). However, CT is also limited by the evolution of hardware and progress in informatics.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Zhang

Tian

Dong

et al. 2017

Clinical Cancer Research

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414

328

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Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n ¼ 88; validation cohort: n ¼ 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 Â 10 À9 ). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 Â 10 À7 ). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages. Conclusions: Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Zhang

Tian

Dong

et al. 2017

Clinical Cancer Research

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414

328

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“…Recent studies have demonstrated that radiomics signatures could be used to determine the risk of lymph-node metastasis in patients with colorectal and bladder cancer (23,24). Radiomics signatures have been reported to be important pretreatment prognostic predictors for progression-free and overall survival in patients with cancer (25)(26)(27)(28).…”

Section: Discussionmentioning

confidence: 99%

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

Peng

Zhang

et al. 2018

Diagn Interv Radiol

146

119

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H epatocellular carcinoma (HCC) is the sixth most common type of cancer and ranks third as a cause of cancer-related death globally. Worldwide, approximately 630 000 new cases of HCC occur annually, with more than half of these cases occurring in China (1). This disease is related to hepatitis B virus (HBV) infection in almost 70%-90% of cases in the highly endemic Asia-Pacific regions, especially in China (2, 3). Partial hepatectomy and liver transplantation are the most effective curative treatments, albeit in a limited number of cases (4). Indeed, the 5-year recurrence rates after surgical treatment and liver transplantation are as high as 70% and 35%, respectively (5-7). It is, therefore, necessary to find effective biomarkers that can identify aggressive behavior and predict tumor recurrence after liver resection and transplantation.In HCC, the presence of microvascular invasion (MVI) is a histopathologic feature indicative of aggressive behavior (8). Previous studies have identified MVI as a major risk factor for early recurrence in the first two years after liver resection and transplantation (9). Precise identification of MVI involvement in patients with HCC is critical to develop treatment strategies and arrive at prognoses. Over the past decade, researchers have made persistent endeavors towards the preoperative prediction of MVI (10-12). Although several radiologic features on contrast-enhanced magnetic resonance imaging (MRI) and computed tomography (CT) images (such as tumor margin, internal arteries, and hypodense halos) are known PURPOSE We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODSA total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram. RESULTSThe radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction no- CONCLUSIONThe radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC.

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“…In fact, these studies reported high values of accuracy, considering the radiomic signature estimated in a selected population and applied on an independent one from another institute, or from another body region, reporting high values of accuracy. 4,41,107 If this kind of validation is not feasible, some other evaluations should be performed, for example, using test and training sets, which should be taken from a separated cohort or at least considering leave-one-out cross-validation. 9 Even if texture analysis was carried out in the most rigorous way and if significant models that could predict tissue response to radiation or provide a characterization of tumour heterogeneity were found, the biological interpretation of these parameters remains one of the major questions about texture analysis.…”

Section: Limitations Of Texture Analysismentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

BJR

119

104

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

Abstract: This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

Cited by 1,396 publications

References 38 publications

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

Texture analysis of medical images for radiotherapy applications

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