Prediction of histologic grade and type of small (&lt; 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study

Haji-Momenian, Shawn; Ricker, R; Chen, Zirong; Houser, Margaret; Adusumilli, Nagasai; Yang, Myung; Toubaji, Antoun; Loew, Murray H.

doi:10.1007/s00261-021-03044-5

Cited by 3 publications

(4 citation statements)

References 44 publications

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“…These studies were mainly based on SRM for benignmalignant discrimination and extracted features based only on the largest dimension of the tumor and did not include the full 3D-ROI, thus not containing the complete information of the tumor. Hagi-Momenian et al [22] constructed various machine learning models based on noncontrast phase, CMP and NP for histological grading and tumor subtyping of small pRCC, respectively. The models constructed based on the features extracted from CMP had the highest AUC values of 0.97-1.0.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics is a new research method that extracts features from medical images that are not visible to the human eye [18,19]. Radiomics has been successfully in various areas related to SRM, including differentiating between benign and malignant SRM and grading small RCC [20][21][22][23][24]. Most research has focused on textural features, and has not considered the potential value of clinical data and imaging characteristics, which could improve the diagnostic accuracy of the models.…”

Section: Introductionmentioning

confidence: 99%

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Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Gao

Wang

Zhao

et al. 2023

Preprint

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Background To develop and validate a CT-based radiomics nomogram for preoperatively predicting WHO/ISUP nuclear grade in small (< 4 cm) clear cell renal cell carcinoma (ccRCC). Methods A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n=67) and the testing set (n=46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. Results The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. Conclusion The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Gao

Wang

Zhao

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These studies were mainly based on SRM for benign-malignant discrimination and extracted features based only on the largest dimension of the tumor and did not include the full 3D-ROI, thus not containing the complete information of the tumor. Hagi-Momenian et al [ 24 ] constructed various machine learning models based on noncontrast phase, CMP and NP for histological grading and tumor subtyping of small pRCC, respectively. The models constructed based on the features extracted from CMP had the highest AUC values of 0.97-1.0.…”

Section: Discussionmentioning

confidence: 99%

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Gao,

Wang,

Zhao

et al. 2023

BMC Cancer

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Background Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery. Methods A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46). The baseline and CT imaging data of the patients were evaluated statistically to develop a clinical model. A radiomics model was created, and the radiomics score (Rad-score) was calculated by extracting radiomics features from the CT images. Then, a clinical radiomics nomogram was developed using multivariate logistic regression analysis by combining the Rad-score and critical clinical characteristics. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of small ccRCC in both the training and testing sets. Results The radiomics model was constructed using six features obtained from the CT images. The shape and relative enhancement value of the nephrographic phase (REV of the NP) were found to be independent risk factors in the clinical model. The area under the curve (AUC) values for the training and testing sets for the clinical radiomics nomogram were 0.940 and 0.902, respectively. Decision curve analysis (DCA) revealed that the radiomics nomogram model was a better predictor, with the highest degree of coincidence. Conclusion The CT-based radiomics nomogram has the potential to be a noninvasive and preoperative method for predicting the WHO/ISUP grade of small ccRCC.

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“…In our study, the ensemble deep learning model showed good performance for discriminating Xp11.2 tRCC from common RCC subtypes with a test AUC of 0.845, and it also achieved similar or better diagnostic performance than urologists. Additionally, the previous study has shown that deep learning models trained by CT images can accurately distinguish between high and low nuclear grades for ccRCC with the accuracy of 0.82 [24], but more studies displayed that ML models constructed from CT imaging texture features can accurately predict the nuclear grades for ccRCC or pRCC with good performance [25][26][27][28]. We speculated that ML has much better performance than DL for nuclear-grade prediction, but ML based on artificial neural networks showed the greatest accuracy for differentiating low-and high-grade for ccRCCs [29].…”

Section: Performance Compared Between the Ensemble Model And Urologistsmentioning

confidence: 99%

Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

Lu¹,

Ma²,

Dong³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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This study aims to apply ResNet-18 convolutional neural network (CNN) and XGBoost to preoperative computed tomography (CT) images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC) from common subtypes of renal cell carcinoma (RCC) in order to provide patients with individualized treatment plans. Data from 45 patients with Xp11.2 tRCC from January 2007 to December 2021 are collected. Clear cell RCC (ccRCC), papillary RCC (pRCC), or chromophobe RCC (chRCC) can be detected from each patient. CT images are acquired in the following three phases: unenhanced, corticomedullary, and nephrographic. A unified framework is proposed for the classification of renal masses. In this framework, ResNet-18 CNN is employed to classify renal cancers with CT images, while XGBoost is adopted with clinical data. Experiments demonstrate that, if applying ResNet-18 CNN or XGBoost singly, the latter outperforms the former, while the framework integrating both technologies performs similarly or better than urologists. Especially, the possibility of misclassifying Xp11.2 tRCC, pRCC, and chRCC as ccRCC by the proposed framework is much lower than urologists. KEYWORDSResNet-18 CNN; XGBoost; computed tomography; TFE3; renal cell carcinoma Materials and Methods Patient EnrollmentThe complete CT imaging data and postoperative pathological data of patients with Xp11.2 tRCC diagnosed in the Nanjing Drum Tower Hospital were retrospectively analyzed from January 2007 to December 2021. 45 cases of Xp11.2 tRCC with complete clinicopathological data were enrolled in the study, including complete medical histories, clinical information, and pathological data. According to both tumor size and pathological stage, the matched patients included 45 with ccRCC, pRCC, or chRCC, respectively. The study was approved by the Institutional Review Board of Nanjing Drum Tower Hospital. Inclusion criteria:1. Patients underwent radical or partial nephrectomy. 2. Preoperative CT examination of renal tumors and complete examination records. 3. The reviewed pathological diagnosis was consistent with the initial postoperative diagnosis and the pathological diagnosis of Xp11.2 tRCC was validated by the next generation sequence. 4. These patients were without cardiovascular and cerebrovascular diseases and chronic kidney disease that affected the blood supply of the kidney. 5. These patients had a unilateral single renal tumor.

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Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study

Cited by 3 publications

References 44 publications

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

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Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study

Cited by 3 publications

References 44 publications

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (&lt; 4 cm) clear cell renal cell carcinoma

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (&lt; 4 cm) clear cell renal cell carcinoma

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

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Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma