Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

Peng, Jie; Lu, Fangyang; Huang, Jinhua; Zhang, Jing; Gong, Weijuan; Hu, Yong; Wang, Jun

doi:10.3389/fonc.2022.853254

Cited by 5 publications

(2 citation statements)

References 42 publications

(40 reference statements)

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“…The radiomics features analysis was conducted using four specimens. Masks were employed to crop the peripheral zone and central zone, and radiomic features for both zones were generated using pyradiomics [18][19][20][21]. Table 1 illustrates the comparison of peripheral zone and central zone for all four specimens, utilizing heatmaps to represent features, where red corresponds to high values and green to low values.…”

Section: Comparison Radiomic Features Of Two Zonesmentioning

confidence: 99%

Feature extraction of ultrasound radiofrequency data for the classification of the peripheral zone of human prostate

Pathour,

Ma,

Strand

et al. 2024

Medical Imaging 2024: Ultrasonic Imaging and Tomography

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Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using ex-vivo whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.

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Section: Comparison Radiomic Features Of Two Zonesmentioning

confidence: 99%

Feature extraction of ultrasound radiofrequency data for the classification of the peripheral zone of human prostate

Pathour,

Ma,

Strand

et al. 2024

Medical Imaging 2024: Ultrasonic Imaging and Tomography

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“…Gene Set Enrichment Analysis was used to determine the association between the DL model and signal pathways and genomics expression [23]. The radiological data from 31 patients with LUSC were collected from The Cancer Imaging Archive (www.Cancer.Imaging.…”

Section: Genomics Analysismentioning

confidence: 99%

Deep Learning Based on Computed Tomography Predicts Response to Chemoimmunotherapy in Lung Squamous Cell Carcinoma

2024

Aging dis

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Non-small-cell lung carcinoma (NSCLC) often carries a dire prognosis. The advent of neoadjuvant chemoimmunotherapy (NCI) has become a promising approach in NSCLC treatment, making the identification of reliable biomarkers for major pathological response (MPR) crucial. This study aimed to devise a deep learning (DL) model to estimate the MPR to NCI in lung squamous cell carcinoma (LUSC) patients and uncover its biological mechanism. We enrolled a cohort of 309 LUSC patients from various medical institutions. A ResNet50 model, trained on contrast-enhanced computed tomography images, was developed, and validated to predict MPR. We examined somatic mutations, genomic data, tumor-infiltrating immune cells, and intra-tumor microorganisms. Post-treatment, 149 (48.22%) patients exhibited MPR. The DL model demonstrated excellent predictive accuracy, evidenced by an area under the receiver operating characteristic curve (AUC) of 0.95 (95% CI: 0.98-1.00) and 0.90 (95% CI: 0.81-0.98) in the first and second validation sets, respectively. Multivariate logistic regression analysis identified the DL model score (low vs. high) as an independent predictor of MPR. The prediction of MPR (P-MPR) correlated with mutations in four genes, as well as gene ontology and pathways tied to immune response and antigen processing and presentation. Analysis also highlighted diversity in immune cells within the tumor microenvironment and in peripheral blood. Moreover, the presence of four distinct bacteria varied among intra-tumor microorganisms. Our DL model proved highly effective in predicting MPR in LUSC patients undergoing NCI, significantly advancing our understanding of the biological mechanisms involved.

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Integrating IASLC grading and radiomics for predicting postoperative outcomes in stage IA invasive lung adenocarcinoma

Chen,

Wu,

You

et al. 2024

Medical Physics

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BackgroundThe International Association for the Study of Lung Cancer (IASLC) Pathology Committee introduced a histologic grading system for invasive lung adenocarcinoma (LUAD) in 2020. The IASLC grading system, hinging on the evaluation of predominant and high‐grade histologic patterns, has proven to be practical and prognostic for invasive LUAD. However, there are still limitations in evaluating the prognosis of stage IA LUAD. Radiomics may serve as a valuable complement.PurposeTo establish a model that integrates IASLC grading and radiomics, aimed at predicting the prognosis of stage IA LUAD.MethodsWe conducted a retrospective analysis of 628 patients diagnosed with stage IA LUAD who underwent surgical resection between January 2015 and December 2018 at our institution. The patients were randomly divided into the training set (n = 439) and testing set (n = 189) at a ratio of 7:3. Overall survival (OS) and disease‐free survival (DFS) were taken as the end points. Radiomics features were obtained by PyRadiomics. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). The prediction models for OS and DFS were developed using multivariate Cox regression analysis, and the models were visualized through nomogram plots. The model's performance was evaluated using area under the curves (AUC), concordance index (C‐index), calibration curves, and survival decision curve analysis (DCA).ResultsIn total, nine radiomics features were selected for the OS prediction model, and 15 radiomics features were selected for the DFS prediction model. Patients with high radiomics scores were associated with a worse prognosis (p < 0.001). We built separate prediction models using radiomics or IASLC alone, as well as a combined prediction model. In the prediction of OS, we observed that the combined model (C‐index: 0.812 ± 0.024, 3 years AUC: 0.692, 5 years AUC: 0.792) achieved superior predictive performance than the radiomics (C‐index: 0.743 ± 0.038, 3 years AUC: 0.633, 5 years AUC: 0.768) and IASLC grading (C‐index: 0.765 ± 0.042, 3 years AUC: 0.658, 5 years AUC: 0.743) models alone. Similar results were obtained in the models for DFS.ConclusionThe combination of radiomics and IASLC pathological grading proves to be an effective approach for predicting the prognosis of stage IA LUAD. This has substantial clinical relevance in guiding treatment decisions for early‐stage LUAD.

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Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

Cited by 5 publications

References 42 publications

Feature extraction of ultrasound radiofrequency data for the classification of the peripheral zone of human prostate

Feature extraction of ultrasound radiofrequency data for the classification of the peripheral zone of human prostate

Deep Learning Based on Computed Tomography Predicts Response to Chemoimmunotherapy in Lung Squamous Cell Carcinoma

Integrating IASLC grading and radiomics for predicting postoperative outcomes in stage IA invasive lung adenocarcinoma

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