Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer

Liu, Jinjin; Wang, Xuchao; Hu, Mengshang; Zheng, Yan; Zhu, Lin; Wang, Wei; Hu, Jisu; Zhou, Zhengxin; Dai, Yakang; Dong, Fenglin

doi:10.3389/fonc.2022.963925

Cited by 14 publications

(12 citation statements)

References 41 publications

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“…Additionally, Cheng suggested that the edge characteristics of breast masses on ultrasound imaging can indirectly reflect the expression level of Ki‐67. Liu 32 developed a radiomics nomogram including maximum diameter of lesions, stiff rim sign, US‐reported ALN status, and radiomics signature to predict the expression level of Ki‐67, achieving AUC of 0.904 and 0.890 in the training and validation cohorts, respectively. These findings are consistent with our study, which found that for breast lesions with high expression of Ki‐67, Demetics paid more attention to the edge of the mass ( p < 0.001).…”

Section: Discussionmentioning

confidence: 99%

The value of Demetics ultrasound‐assisted diagnosis system in diagnosis of breast lesions and in assessment Ki‐67 status of breast cancer

Wang,

Nie,

Liu

et al. 2023

J of Clinical Ultrasound

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PurposeThis study aims to explore the diagnostic efficiency of the Demetics for breast lesions and assessment of Ki‐67 status.MaterialThis retrospective study included 291 patients. Three combined methods (method 1: upgraded BI‐RADS when Demetics classified the breast lesion as malignant; method 2: downgraded BI‐RADS when Demetics classified the breast lesion as benign; method 3: BI‐RADS was upgraded or downgraded according to Demetrics' diagnosis) were used to compare the diagnostic efficiency of two radiologists with different seniority before and after using Demetics. The correlation between the visual heatmap by Demetics and the Ki‐67 expression level of breast cancer was explored.ResultsThe sensitivity, specificity, and area under curve (AUC) of diagnosis by Demetics, junior radiologist and senior radiologist were 89.5%, 83.1%, 0.863; 76.9%, 82.4%, 0.797 and 81.1%, 89.9%, 0.855, respectively. Method 1 was the best for senior radiologist, which increased AUC from 0.855 to 0.884. For junior radiologist, Method 3 was the best method, improving sensitivity (88.8% vs. 76.9%) and specificity (87.2% vs. 82.4%). Demetics paid more attention to the peripheral area of breast cancer with high expression of Ki‐67.ConclusionDemetics has shown good diagnostic efficiency in the assisted diagnosis of breast lesions and is expected to further distinguish Ki‐67 status of breast cancer.

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Section: Discussionmentioning

confidence: 99%

The value of Demetics ultrasound‐assisted diagnosis system in diagnosis of breast lesions and in assessment Ki‐67 status of breast cancer

Wang,

Nie,

Liu

et al. 2023

J of Clinical Ultrasound

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“…Feng, S et al extracted radiomics features from dynamic contrast-enhanced magnetic resonance imaging, and their radiomics models reached an AUC of 0.839 (95% con dence interval [CI], 0.768-0.895) within the training set and 0.795 (95% CI, 0.674-0.887) within the independent validation set in the prediction of Ki-67 status 20 . Liu et al constructed a radiomics model based on ultrasound images, and their results showed an AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts 21 . For our study, the metabolic information derived from PET/CT images were expressed as multiple radiomics features, which were used to predict the Ki67 status, and showed an AUC of 0.83[0.74 0.92] within the training set and 0.75[0.56 0.95] within the testing set.…”

Section: Discussionmentioning

confidence: 99%

PET/CT Radiomics Integrated with Clinical Indexes as A Tool to Predict Ki67 in Breast Cancer: A Pilot Study

Ding

Liang

et al. 2023

Preprint

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Background: Breast cancer (BC) represents the second cause of cancer-related death among women globally, and Ki67 was demonstrated as an important predictive biomarker in worse survival and neoadjuvant treatment in BC. This study aims to investigate the value of radiomics features derived from 18F-FDG PET/CT combined with clinical characteristics in predicting Ki67 in patients with BC. Methods: A total of 114 patients diagnosed as BC and examined using 18F-FDG PET/CT were included in this study. Patients were randomly separated into a training set (n = 79, with 55 cases of Ki67 + and 24 cases of Ki67-) and a validation set (n = 35, with 24 cases of Ki67 + and 11 cases of Ki67-) at a ratio of 7:3. Thirteen clinical characteristics and 704 radiomics features were extracted, and the univariance logistic analysis, max-Relevance and Min-Redundancy, the least absolute shrinkage and selection operator regression, and the Spearman test were applied for feature selection. Three models were developed, including the clinical model, the radiomics model, and the combined model, and a nomogram of the combined model was constructed. The predictive performance of all three models was examined by the receiver operating characteristic (ROC) curve. Clinical utility was validated by decision curve analysis (DCA). Results: The N stage, tumor morphology, maximal standard uptake value, and the longest diameter were significantly different in Ki67 + and Ki67- groups (P < 0.05) and were selected as the most discriminative clinical features. Eight radiomics features were selected for the radiomics model. In total, 7 radiomics and the above 4 clinical characteristics were selected for the combined model. The AUC of the combined model in the training and test group was 0.90 (95% Confidence Interval (CI): 0.82–0.97) and 0.81 (95% CI: 0.64–0.99), respectively. The combined model significantly outperformed the radiomics model and the clinical model alone (P < 0.05). The DCA curve showed the advantages of the combined model over the clinical model and radiomics model. Conclusions: The radiomics-derived features combined with the clinical features could effectively predict Ki67 expression in BC based on PET/CT images. Trial registration: This study was registered at ClinicalTrials Gov (number NCT05826197) on 7th, May 2023.

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“…These are then transformed into a high-dimensional dataset to be combined with the patient’s clinical information in the construction of a prediction model ( 14 ). Considerable progress has thus far been made in the differentiation of benign from malignant masses ( 5 , 6 ), the prediction of lymph node metastasis ( 15 , 16 ), the determination of molecular typing ( 17 , 18 ), and the evaluation of response to adjuvant chemotherapy ( 19 , 20 ). In addition to US, radiomics has also been used with magnetic resonance imaging (MRI) and X-rays in the development of various breast cancer predictive models, showing promising results in the noninvasive prediction of lesion malignancy ( 21 , 22 ).…”

Section: Introductionmentioning

confidence: 99%

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors

Qin,

Duan

et al. 2024

Transl Cancer Res

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Background Early diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction. Methods We retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group. Results Finally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852–0.942] and 0.85 (95% CI: 0.775–0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918–0.981) and 0.92 (95% CI: 0.866–0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value. Conclusions The combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.

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Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer

Cited by 14 publications

References 41 publications

The value of Demetics ultrasound‐assisted diagnosis system in diagnosis of breast lesions and in assessment Ki‐67 status of breast cancer

The value of Demetics ultrasound‐assisted diagnosis system in diagnosis of breast lesions and in assessment Ki‐67 status of breast cancer

PET/CT Radiomics Integrated with Clinical Indexes as A Tool to Predict Ki67 in Breast Cancer: A Pilot Study

Potential added value of computed tomography radiomics to multimodal prediction models for benign and malignant breast tumors

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