The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study

Yang, Guangli; Nie, Pei; Yan, Lei; Zhang, Mingxin; Wang, Yangyang; Zhao, Lianzi; Li, Mingyao; Xie, Fei; Xie, Haizhu; Li, Xianjun; Fawei, Xiang; Wang, Nan; Cheng, Nan; Zhao, Xia; Wang, Ning; Wang, Yicong; Chen, Chengcheng; Yun, Canhua; Cui, Jingjing; Duan, Shaofeng; Zhang, Ran; Hao, Dapeng; Wang, Ximing; Wang, Zhenguang; Niu, Haitao

doi:10.1007/s00259-022-05773-1

Cited by 34 publications

(13 citation statements)

References 31 publications

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“…Meanwhile, the weight coefficient of the signature Difference Variance in RS1 was 0.045261, while it did not have any weight in RS2, suggesting that the effect of Difference Variance was diminished by NAC. The main reason for the above differences is the high tumor heterogeneity and disordered tumor cell arrangement before NAC treatment in breast cancer ( 25 , 26 ), which is reflected in the RSs of pixel grayscale and texture inhomogeneity ( 27 ). After NAC treatment, patients with pCR will have a higher necrosis rate of tumor cells, lower tumor heterogeneity, and a uniform internal tissue structure, while patients with non-pCR will have less tumor cell necrosis and high tumor heterogeneity, which is not different than before treatment ( 28 , 29 ).…”

Section: Discussionmentioning

confidence: 99%

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer

Liu,

Leng,

Liu

et al. 2024

Front. Oncol.

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IntroductionWe aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features.MethodsUltrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve.ResultsWe found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value.DiscussionThe Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

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Section: Discussionmentioning

confidence: 99%

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer

Liu,

Leng,

Liu

et al. 2024

Front. Oncol.

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“…Multiple studies have developed diverse radiomics models for the prediction of recurrence and metastasis in clear cell carcinoma [19,[27][28][29]. For example, Bing Kang et al created a radiomic model for use in T1 stage ccRCC consisting of ten intratumoral features for the prediction of postoperative recurrence risks [28], and achieve a high AUC (training, 0.91; validation, 0.92).…”

Section: Discussionmentioning

confidence: 99%

“…It has shown potential in distinguishing between benign and malignant renal tumors [10], different subtypes [11,12], grading and staging [13][14][15][16] and survival [17,18]. A recent multicenter study [19] utilized radiomics to assess the recurrence and metastasis of localized renal cell carcinoma. The study found that combining radiomics with existing prognostic models improved predictive capability (with a C-index of 0.74-0.78).…”

Section: Introductionmentioning

confidence: 99%

Multiregional radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

Li,

Guo,

Wang

et al. 2024

Preprint

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Background The selection of individualized treatment options based on the risk of recurrence is crucial in the adjuvant treatment of clear cell renal cell carcinoma(ccRCC). Multiregional radiomics might noninvasively obtain potential information about the intratumoral and peritumoral heterogeneity of ccRCC and reveal the prognostic information behind the images. This study aimed to develop a CT-based multiregional radiomics nomogram to improve the stratification of postoperative recurrence risk in patients with localized ccRCC. Methods A total of 395 patients with pathologically diagnosed ccRCC were included in the training (n = 281) and internal validation set (n = 114). Multiregion radiomics features from both the intratumoral and peritumoral areas were extracted. The selection of radiomics features and clinicopathological factors was performed using the least absolute shrinkage and selection operator (LASSO) Cox regression. A final model (FM) for the radiomics nomogram was developed, which incorporated the selected clinicopathological and radiomics features predictors based on multivariate Cox proportional hazard regression. The performance of the model was assessed using receiver operator characteristic (ROC) analysis. Results The radiomics nomogram demonstrated excellent prediction performance in both the training and validation sets. The discriminatory ability of the radiomics nomogram was superior to that of the clinical model (C-index 0.926 vs .0.898, P < 0.05). Decision curve analysis revealed that the nomogram had more net benefit than the clinical model. Conclusions The use of a radiomic nomogram with multiregion features improved the stratification of postoperative recurrence risk in patients with localized ccRCC and can be considered a valuable tool in clinical decision-making.

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“…In recent years, data-driven machine learning and DL have been widely used in the processing and analysis of medical images, providing new tools for disease diagnosis and prognosis prediction. A novel approach combining radiomics and machine learning has brought encouraging results in the diagnosis and prediction of urological cancers [ 38 , 39 , 40 ]. Previously, our team developed a DL-model based on cystoscopy for clinical real-time recognition of bladder tumors with an accuracy comparable to that of experienced clinical experts [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

Zheng

Yang

et al. 2022

Cancers

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(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.

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The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study

Cited by 34 publications

References 31 publications

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer

Multiregional radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

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