Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics

Chong, Gun Oh; Park, Shin‐Hyung; Park, Nora Jee-Young; Bae, Bong Kyung; Lee, Yoon Hee; Jeong, Shin Young; Kim, Jae-Chul; Park, Ji Young; Ando, Yasunobu; Han, Hyung Soo

doi:10.3390/cancers13205140

Cited by 8 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies on non-invasive prediction of tumor budding are scarce. A recent study by Chong et al reported that MR-based radiomic features from T2-weighted imaging and contrast-enhanced T1-weighted imaging are associated with tumor budding status in cervical cancer patients treated with radical hysterectomy [ 15 ]. Li et al in addition also integrated DW images into their multi-sequence radiomics model, which was able to predict the tumor budding status in locally advanced rectal cancer [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

“…To the present, only a few studies have linked radiological imaging features with the presence and extent of tumor budding in various cancers. Chong et al and Li et al correlated radiological imaging features of cervical and rectal cancer with the tumor budding status using Radiomics [ 15 , 16 ]. Radiomics is an advanced quantitative imaging analysis method that extracts features from radiological images that are difficult to appreciate with the naked eye [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

Mayer,

Hausen,

Steinle

et al. 2024

Langenbecks Arch Surg

View full text Add to dashboard Cite

Purpose Pancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors. Methods We analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients). Results 64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352). Conclusion Our study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

Mayer,

Hausen,

Steinle

et al. 2024

Langenbecks Arch Surg

View full text Add to dashboard Cite

show abstract

“…Normalization using the z-score method was performed to ensure that all features have a mean of 0 and a standard deviation (SD) of 1, as described in previous studies ( 24 ). Feature selection, dimensionality reduction, and prediction model construction were conducted using the feature data from the training group.…”

Section: Methodsmentioning

confidence: 99%

“…Few studies have reported on predicting tumor budding. In recent years, some scholars have begun to explore this area ( 23 , 24 ). Currently, only Li et al.…”

Section: Introductionmentioning

confidence: 99%

Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics

Qu,

Zhang,

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

ObjectiveThis study aimed to explore the radiomics model based on magnetic resonance imaging (MRI) T2WI and compare the value of different machine algorithms in preoperatively predicting tumor budding (TB) grading in rectal cancer.MethodsA retrospective study was conducted on 266 patients with preoperative rectal MRI examinations, who underwent complete surgical resection and confirmed pathological diagnosis of rectal cancer. Among them, patients from Qingdao West Coast Hospital were assigned as the training group (n=172), while patients from other hospitals were assigned as the external validation group (n=94). Regions of interest (ROIs) were delineated, and image features were extracted and dimensionally reduced using the Least Absolute Shrinkage and Selection Operator (LASSO). Eight machine algorithms were used to construct the models, and the diagnostic performance of the models was evaluated and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as clinical utility assessment using decision curve analysis (DCA).ResultsA total of 1197 features were extracted, and after feature selection and dimension reduction, 11 image features related to TB grading were obtained. Among the eight algorithm models, the support vector machine (SVM) algorithm achieved the best diagnostic performance, with accuracy, sensitivity, and specificity of 0.826, 0.949, and 0.723 in the training group, and 0.713, 0.579, and 0.804 in the validation group, respectively. DCA demonstrated the clinical utility of this radiomics model.ConclusionThe radiomics model based on MR T2WI can provide an effective and noninvasive method for preoperative TB grading assessment in patients with rectal cancer.

show abstract

“…Surgery after NACT was conducted for patients in the investigation cohort; individuals with poor responses were advised to receive direct concurrent chemoradiotherapy. Many scholars have used radiomics to predict risk factors marking the postoperative period in patients with cervical cancer [15][16][17][18] . However, intermediary-and high-risk factors have not been predicted in patients who undergo surgery after NACT.…”

Section: Introductionmentioning

confidence: 99%

Delta Radiomics Analysis for Prediction of Intermediary- and High-Risk Factors for Patients with Locally Advanced Cervical Cancer Receiving Neoadjuvant Therapy

Zhou

Xie

et al. 2023

Preprint

View full text Add to dashboard Cite

OBJECTIVE: This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA+C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. MATERIALS AND METHODS: A total of 157 patients were divided into two groups, namely, those without high-risk factors and the absence or presence of any intermediary-risk factors (negative set; n = 75) and those with more than one high-risk factor or more than two intermediary-risk factors (positive set; n = 82). Radiomics characteristics were extracted using Ax-LAVA+C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann-Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models—the clinical model, the radiomics model, and the combined clinic and radiomics model—were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. RESULTS: Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. CONCLUSION: The use of machine learning algorithms to analyze Delta Ax LAVA+C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy.

show abstract

Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics

Cited by 8 publications

References 37 publications

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics

Delta Radiomics Analysis for Prediction of Intermediary- and High-Risk Factors for Patients with Locally Advanced Cervical Cancer Receiving Neoadjuvant Therapy

Contact Info

Product

Resources

About