Radiomics based on <sup>18</sup>F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study

Ou, Xuejin; Zhang, Jing; Wang, Jian; Pang, Fuwen; Wang, Yongsheng; Wei, Xiawei; Ma, Xuelei

doi:10.1002/cam4.2711

Cited by 43 publications

(33 citation statements)

References 28 publications

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“…We evaluated 20 publications on breast cancer [ 59 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 ], all of them using FDG as the radiotracer. The average number of patients was 118 (median = 81, range, 34–435).…”

Section: Resultsmentioning

confidence: 99%

“…In one of the most relevant works assessing diagnosis, Ou et al [ 194 ] evaluated the ability of PET and CT radiomics to differentiate breast carcinoma from breast lymphoma using a machine-learning approach. They validated their findings in a separate subsample of the cohort, obtaining an AUC of 0.81 for PET radiomics, which outperformed CT radiomics.…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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“…To date, studies that have analyzed radiomic features based on [ 18 F]FDG-PET/CT in patients with breast cancer have focused on building models to predict pathological complete response to neoadjuvant chemotherapy or to differentiate breast carcinoma from breast lymphoma [ 20 , 22 , 65 ]. Antunovic et al reported AUCs of 0.70–0.73 across all predictive models [ 22 ]; Li et al reported AUCs of 0.72 and 0.73, respectively, without and with patient age incorporated [ 20 ]; and Ou et al reported AUCs of 0.81 and 0.76 for PET and CT models, respectively [ 65 ]. Huang et al [ 66 ] reported mean AUCs of 0.75 and 0.68 for one-year and two-year recurrence-free survival, respectively, from using PET/MRI-based models.…”

Section: Discussionmentioning

confidence: 99%

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

Krajnc

Papp

Nakuz

et al. 2021

Cancers

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Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.

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“…Regarding PET imaging, the role of this functional technique has been explored in breast cancer mainly for prognostic/therapeutic purposes, particularly in the early prediction of the response to neoadjuvant chemotherapy [39][40][41]. In a recently published study, the usefulness of radiomics and ML applied to PET/CT to differentiate breast carcinoma from lymphoma was investigated in a small number of lesions (19 breast lymphoma and 25 breast cancer lesions) [42]. Different predictive models were built using combinations of clinical data, quantitative parameters (SUV), radiomics features ( rst and second order parameters extracted from both PET and CT images) and CT images.…”

Section: Discussionmentioning

confidence: 99%

AI-enhanced Synchronized Multiparametric 18F-FDG PET/MRI for Accurate Breast Cancer Diagnosis

Romeo

Clauser

Rasul

et al. 2021

Preprint

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Purpose: to assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from synchronized multiparametric 18F-FDG PET/MRI images can differentiate benign and malignant breast lesions.Methods: 102 consecutive patients with 120 BI-RADS 0, 4 and 5 breast lesions (101 malignant, 19 benign) detected by ultrasound and/or mammography were prospectively enrolled and underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters and radiomics features were extracted from dynamic contrast-enhanced (MTT, VD, PF), diffusion (ADCmean of breast lesions and contralateral breast parenchyma), PET (SUVmax, mean and minimum of breast lesions, SUVmean of uni- and contralateral breast parenchyma) and T2-w images. Different diagnostic models were developed using a fine gaussian support vector machine algorithm and exploring different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating benign from malignant breast lesions using a 5-fold cross validation. The performance of the best radiomics and ML model was compared with that of expert readers review physician using the McNemar test.Results: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983) and was higher (AUC 0.868) yet not significant to expert readers review (p=0.508).Conclusion: A radiomics and ML model combining quantitative parameters and radiomics features extracted from synchronized multiparametric 18F-FDG PET/MRI images can accurately discriminate benign from malignant breast lesions.

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Radiomics based on ¹⁸F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study

Cited by 43 publications

References 28 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

AI-enhanced Synchronized Multiparametric 18F-FDG PET/MRI for Accurate Breast Cancer Diagnosis

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Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study

Cited by 43 publications

References 28 publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

AI-enhanced Synchronized Multiparametric 18F-FDG PET/MRI for Accurate Breast Cancer Diagnosis

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Radiomics based on ¹⁸F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study