Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients

Gao, Yuanjing; Luo, Yanwen; Zhao, Chenyang; Xiao, Mengsu; Ma, Li; Li, Wenbo; Qin, Jing; Zhu, Qingli; Jiang, Yuxin

doi:10.1007/s00330-020-07181-1

Cited by 47 publications

(44 citation statements)

References 29 publications

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“…Concerning the identification of key prognostic factors, such as nodal involvement and HER-2 status, Radiomics was tested as a non-invasive imaging biomarker, combined with clinical data, reaching good performance [ 52 , 53 , 54 ]. Gao Y. et al [ 52 ] tested a radiomic US-based nomogram in 343 patients affected by T1/T2 invasive BC by adding patient age and tumor size. The model showed good performance to detect nodal metastases in both the training and validation cohorts (0.846 and 0.733 AUC, respectively) by analyzing primary breast cancers.…”

Section: Breast Cancermentioning

confidence: 99%

Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Polici

Zerunian

Pucciarelli

et al. 2021

Cancers

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Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.

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Section: Breast Cancermentioning

confidence: 99%

Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Polici

Zerunian

Pucciarelli

et al. 2021

Cancers

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show abstract

“… 79 Several nomograms have been created using ultrasound radiomics and LASSO regression to predict the presence of lymph node metastasis with high AUCs. 80 , 81 With the application of deep learning to ultrasound radiomics datasets, known as deep learning radiomics ultrasonography (DLRU), axillary lymph node status may be accurately predicted with AUC of >0.90. 82 , 83 Although radiomics is still in its infancy, it has great potential to pave the way for the personalization of surgical management of the axilla and “virtual biopsies.”…”

Section: New Applications and Technologiesmentioning

confidence: 99%

Staging of the Axilla in Breast Cancer and the Evolving Role of Axillary Ultrasound

Chen¹,

Gillanders²

2021

BCTT

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Axillary lymph nodes have long been recognized as a route for breast cancer to spread systemically. As a result, staging of the axilla has always played a central role in the treatment of breast cancer. Anatomic staging was believed to be important for two reasons: 1) it predicts prognosis and guides medical therapy, and 2) it is a potential therapy for removal of disease in the axilla. This paradigm has now been called into question. Prognostic information is driven increasingly by tumor biology, and trials such as the ACOSOG Z0011 demonstrates removal of axillary disease is not therapeutic. Staging of the axilla has undergone a dramatic de-escalation; however, sentinel lymph node biopsy (SLNB) is still an invasive surgery and represents a large economic burden on the healthcare system. In this review, we outline the changing paradigms of axillary staging in breast cancer from emphasis on anatomic staging to tumor biology, and the evolving role of axillary ultrasound, bringing patients less invasive and more personalized therapy.

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“…Radiomics is an emerging methodology concerned with the quantitative analysis of large-scale medical imaging data, and it can automatically lter comprehensive data obtained from an image [6,7]. Recent advances in radiomics-based US identi ed increasingly gaining ground in oncology to improve diagnosis, assessment of therapeutic response, and disease prediction [8][9][10][11][12]. Luo et al presented that the nomogram incorporating radimoics score and BI-RADS exhibited a potential diagnostic capacity in benign and malignant masses, especially in the BI-RADS category 4 or 5 [11].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma

Zha

Wang

et al. 2022

BJR

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Objective: This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma. Methods: A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA). Results: The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975–0.997) and validation cohort (AUC 0.977, 95% CI, 0.953–1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram. Conclusion: The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models. Advances in knowledge: Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

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Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients

Cited by 47 publications

References 29 publications

Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Staging of the Axilla in Breast Cancer and the Evolving Role of Axillary Ultrasound

Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma

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