Differentiation of Lung Metastases Originated From Different Primary Tumors Using Radiomics Features Based on CT Imaging

Path planning algorithm for percutaneous puncture lung mass biopsy procedure based on the multi-objective constraints and fuzzy optimization

Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience.Approach.To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning.Main results.To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs.Significance.The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure.

Radiomics Model for Predicting FOXP3 Expression Level and Survival in Clear Cell Renal Carcinoma

RATIONALE AND OBJECTIVES

We aimed to evaluate the predictive significance of forkhead box protein 3 (FOXP3) expression levels among individuals with clear cell renal carcinoma (ccRCC) and establish a radiomics model for predicting FOXP3 expression.

MATERIALS AND METHODS

430 patients with ccRCC were included in the gene-based prognostic analyses; 100 samples were used for radiomics feature generation, model development, and evaluation. A gradient boosting machine was employed to model the selected radiomics features. The developed model generated radiomics scores (RS) that predicted FOXP3 expression. The FOXP3 prognostic model combining imaging features was applied for survival and clinical indicator correlation analyses.

RESULTS

FOXP3 was highly expressed in patients with ccRCC and served as an independent predictive marker (hazard ratio [HR]=2.357, 95% CI [confidence interval]: 1.582-3.511, p < 0.001). The radiomics model formed by three radiomics characteristics was identified as a strong prognostic indicator of overall survival (OS). The predictive power of the model was commendable (areas under the curve: 0.835 and 0.809 for training and validation sets, respectively). Significant between-group variations in RS distribution were identified, as indicated by gene expression levels (p < 0.05). Disparities were observed in pathological stage, pharmaceutical therapy, and neoplasm status between low and high RS cohorts (p < 0.001). Kaplan-Meier curves revealed a significant correlation between increased RS and decreased OS (p = 0.001), which was also observed in the multivariate analyses (HR=3.411, 95% CI: 1.039-11.196, p = 0.043).

CONCLUSION

Prognostic outcome of ccRCC is closely linked to FOXP3 expression level. Computed tomography-based radiomics shows promise for prognostic prediction in ccRCC.

Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study

Background

Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow.

Methods

We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105: 840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed.

Findings

ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899-0.931) and 0.923 (95% [CI]: 0.905-0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794-0.836) and 0.818 (95% [CI]: 0.790-0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUC of 0.761 (95% [CI]: 0.657-0.842), which verify the model's diagnostic expandability.

Interpretation

Our study established an AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasic CT images.

Funding

National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Medical Image Processing.

Preoperatively Predicting Ki67 Expression in Pituitary Adenomas Using Deep Segmentation Network and Radiomics Analysis Based on Multiparameter MRI

RATIONALE AND OBJECTIVES

Ki67 proliferation index is associated with more aggressive tumor behavior and recurrence of pituitary adenomas (PAs). Recently, radiomics and deep learning have been introduced into the study of pituitary tumors. The present study aimed to investigate the feasibility of predicting the Ki67 proliferation index of PAs using the deep segmentation network and radiomics analysis based on multiparameter MRI.

MATERIALS AND METHODS

First, the cfVB-Net autosegmentation model was trained; subsequently, its performance was evaluated in terms of the dice similarity coefficient (DSC). In the present study, 1214 patients were classified into the high Ki67 expression group (HG) and the low Ki67 expression group (LG). Analyses of three classification models based on radiomics features were performed to distinguish HG from LG. Clinical factors, imaging features, and Radscores were collectively used to create a nomogram in order to effectively predict Ki67 expression.

RESULTS

The cfVB-Net segmentation model demonstrated good performance (DSC: 0.723-0.930). Overall, 18, 15, and 11 optimal features in contrast-enhanced (CE) T1WI, T1WI, and T2WI were obtained for differentiating between HG and LG, respectively. Notably, the best results were presented in the bagging decision tree when CE T1WI and T1WI were combined (area under the receiver operating characteristic curve: training set, 0.927; validation set, 0.831; and independent testing set, 0.825). In the nomogram, age, Hardy' grade, and Radscores were identified as risk predictors of high Ki67 expression.

CONCLUSION

The deep segmentation network and radiomics analysis based on multiparameter MRI exhibited good performance and clinical application value in predicting the expression of Ki67 in PAs.

Do resected colorectal cancer patients need early chest imaging? Impact of clinicopathologic characteristics on time to development of pulmonary metastases

BACKGROUND AND OBJECTIVES

For patients with colorectal cancer (CRC), the lung is the most common extra-abdominal site of distant metastasis. However, practices for chest imaging after colorectal resection vary widely. We aimed to identify characteristics that may indicate a need for early follow-up imaging.

METHODS

We retrospectively reviewed charts of patients who underwent CRC resection, collecting clinicopathologic details and oncologic outcomes. Patients were grouped by timing of pulmonary metastases (PM) development. Analyses were performed to investigate odds ratio (OR) of PM diagnosis within 3 months of CRC resection.

RESULTS

Of 1600 patients with resected CRC, 233 (14.6%) developed PM, at a median of 15.4 months following CRC resection. Univariable analyses revealed age, receipt of systemic therapy, lymph node ratio (LNR), lymphovascular and perineural invasion, and KRAS mutation as risk factors for PM. Furthermore, multivariable regression showed neoadjuvant therapy (OR: 2.99, p < 0.001), adjuvant therapy (OR: 6.28, p < 0.001), LNR (OR: 28.91, p < 0.001), and KRAS alteration (OR: 5.19, p < 0.001) to predict PM within 3 months post-resection.

CONCLUSIONS

We identified clinicopathologic characteristics that predict development of PM within 3 months after primary CRC resection. Early surveillance in such patients should be emphasized to ensure timely identification and treatment of PM.

Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes

This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step approach: in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.

CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies.

MATERIALS AND METHODS

PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity.

RESULTS

In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs.

CONCLUSION

CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.

Radiomics in Lung Metastases: A Systematic Review

Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.