CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules

Development and validation of an artificial intelligence prediction model and a survival risk stratification for lung metastasis in colorectal cancer from highly imbalanced data: A multicenter retrospective study

BACKGROUND

To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients.

METHODS

The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. In addition, stratified LM patients by risk score were utilized for survival analysis.

RESULTS

Extremely low rates of LM with 2.59% and 4.50% were present in the development and validation set. As the imbalanced learning strategy, the BRF model with an Area under the receiver operating characteristic curve (AUC) of 0.874 and an average precision (AP) of 0.184 performed best compares with other models and clinical predictor. Patients with LM in the high-risk group had significantly poorer survival (P＜0.001) and failed to benefit from resection (P = 0.125).

CONCLUSIONS

In summary, we have utilized the BRF algorithm to develop an effective, non-invasive, and practical model for predicting LM in CRC patients based on highly imbalanced datasets. In addition, we have implemented a novel approach to stratify the survival risk of CRC patients with LM based the output of the model.

Multiple cavitary lung lesions from colorectal cancer responding to chemotherapy

Lung metastasis is an uncommon cause of multiple cavitary lung lesions. Herein, we report a case of multiple cavitary lung lesions of colorectal cancer that responded to chemotherapy. An 81-year-old woman was referred to our hospital for abdominal pain. Computed tomography revealed multiple cavitary lung lesions. The patient was diagnosed with lung metastases from colorectal cancer with a lower gastrointestinal endoscopy and bronchoscopy. Following chemotherapy, the cavitary lung lesions shrank. Lung metastases from colorectal cancer may appear as multiple cavitary lung lesions, which may be misdiagnosed as infections. Clinicians should consider lung metastases when multiple cavitary lung lesions are detected.

Comparison of Ultrashort TE Lung MRI and HRCT Lungs for Detection of Pulmonary Nodules in Oncology Patients

Purpose The purpose of this study is to evaluate the detection rate of pulmonary nodules in ultrashort echo time (UTE) lung magnetic resonance imaging (MRI) and to compare it with computed tomography (CT) in oncology patients.

Materials and Methods All individuals undergoing radiotherapy/chemotherapy/regular follow-up or visiting the oncology department and referred to radiology department for nodule detection, during the period of 1 year, were subjected to UTE lung MRI using the sequence Flash 3d_spiralvibe coronal 1.25 mm iso and high-resolution CT lungs and the images were analyzed.

Results Among the total number of nodules detected in both lungs of all patients, nodules detected by CT were 241, and nodules detected by MRI were 212. The nodule detection rate by MRI was 87.96%. The detection rate of nodules for size equal to or more than 5 mm was nearly 100%. For nodules less than 5 mm, and equal to or more than 4 mm, MRI showed a comparable detection rate of 75%, while for nodules less than 4 mm, the detection rate was only 25%.

Conclusion Our study results indicate that lung MRI had a near-complete detection rate for nodules equal to or more than 5 mm in size. Hence, in oncology patients who are undergoing regular follow-up of the lung nodules, lung MRI using UTE can replace low-dose CT, which in turn reduces the radiation dose to the patient.

Nomograms predicting cancer-specific survival for stage IV colorectal cancer with synchronous lung metastases

This study aimed to establish a nomogram for the prediction of cancer-specific survival (CSS) of CRC patients with synchronous LM. The final prognostic nomogram based on prognostic factors was evaluated by concordance index (C-index), time-dependent receiver operating characteristic curves, and calibration curves. In the training and validation groups, the C-index for the nomogram was 0.648 and 0.638, and the AUC was 0.793 and 0.785, respectively. The high quality of the calibration curves in the nomogram models for CSS at 1-, 3-, and 5-year was observed. The nomogram model provided a conventional and useful tool to evaluate the 1-, 3-, and 5-year CSS of CRC patients with synchronous LM.

Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones

Objective

To confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features.

Materials and Methods

Twenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated.

Results

On imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%).

Conclusions

The imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.

An Exploratory Study on the Stable Radiomics Features of Metastatic Small Pulmonary Nodules in Colorectal Cancer Patients

Objectives

To identify the relatively invariable radiomics features as essential characteristics during the growth process of metastatic pulmonary nodules with a diameter of 1 cm or smaller from colorectal cancer (CRC).

Methods

Three hundred and twenty lung nodules were enrolled in this study (200 CRC metastatic nodules in the training cohort, 60 benign nodules in the verification cohort 1, 60 CRC metastatic nodules in the verification cohort 2). All the nodules were divided into four groups according to the maximum diameter: 0 to 0.25 cm, 0.26 to 0.50 cm, 0.51 to 0.75 cm, 0.76 to 1.0 cm. These pulmonary nodules were manually outlined in computed tomography (CT) images with ITK-SNAP software, and 1724 radiomics features were extracted. Kruskal-Wallis test was performed to compare the four different levels of nodules. Cross-validation was used to verify the results. The Spearman rank correlation coefficient is calculated to evaluate the correlation between features.

Results

In training cohort, 90 features remained stable during the growth process of metastasis nodules. In verification cohort 1, 293 features remained stable during the growth process of benign nodules. In verification cohort 2, 118 features remained stable during the growth process of metastasis nodules. It is concluded that 20 features remained stable in metastatic nodules (training cohort and verification cohort 2) but not stable in benign nodules (verification cohort 1). Through the cross-validation (n=100), 11 features remained stable more than 90 times.

Conclusions

This study suggests that a small number of radiomics features from CRC metastatic pulmonary nodules remain relatively stable from small to large, and they do not remain stable in benign nodules. These stable features may reflect the essential characteristics of metastatic nodules and become a valuable point for identifying metastatic pulmonary nodules from benign nodules.

Dealing with indeterminate pulmonary nodules in colorectal cancer patients; a systematic review

INTRODUCTION

Indeterminate pulmonary nodules (IPNs) are frequently encountered on staging computed tomography (CT) in colorectal cancer (CRC) patients and they create diagnostic dilemmas. This systematic review and pooled analysis aims to estimate the incidence and risk of malignancy of IPNs and provide an overview of the existing literature on IPNs in CRC patients.

MATERIALS AND METHODS

EMBASE, Pubmed and the Cochrane database were searched for papers published between January 2005 and April 2020. Studies describing the incidence of IPNs and the risk of malignancy in CRC patients and where the full text was available in the English language were considered for inclusion. Exclusion criteria included studies that used chest X-ray instead of CT, liver metastasis cohorts, studies with less than 60 CRC patients and reviews.

RESULTS

A total of 18 studies met the inclusion criteria, involving 8637 patients. Pooled analysis revealed IPNs on staging chest CT in 1327 (15%) of the CRC patients. IPNs appeared to be metastatic disease during follow up in 16% of these patients. Regional lymph node metastases, liver metastases, location of the primary tumour in the rectum, larger IPN size and multiple IPNs are the five most frequently reported parameters predicting the risk of malignancy of IPNs.

CONCLUSION

A risk stratification model for CRC patients with IPNs is warranted to enable an adequate selection of high risk patients for IPN follow up and to diminish the use of unnecessary repetitive chest CT-scans in the many low risk patients.