A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

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Abstract

Purpose

This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC).

Methods

This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was used for modeling. The receiver operator characteristic curve and area under the curve (AUC) were calculated to assess the performance of the radiomics-based classifiers.

Results

For each MRI sequence, a total of 863 radiomic features were extracted and the top 30 features were selected for model construction. The radiomic classifiers of tumoral VOI and peritumoral VOI were both promising for predicting pCR, with AUCs of 0.96 and 0.97 in the training cohort and 0.89 and 0.78 in the validation cohort, respectively. The tumoral + peritumoral VOI radiomic model could further improve the predictive accuracy, with AUCs of 0.98 and 0.92 in the training and validation cohorts.

Conclusions

The tumoral and peritumoral multiparametric MRI radiomics model can promisingly predict pCR in breast cancer using MRI images before surgery. Our results highlighted the potential value of the tumoral and peritumoral radiomic model in cancer management.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-022-12034-w.

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Most cited references 34

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Cancer statistics, 2020

Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2020)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.

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Computational Radiomics System to Decode the Radiographic Phenotype

Chintan Parmar, Ahmed Raafat Hosny, Hugo J.W.L. Aerts … (2017)

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.

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Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis.

Patricia Cortazar, Lijun Zhang, Michael Untch … (2014)

Pathological complete response has been proposed as a surrogate endpoint for prediction of long-term clinical benefit, such as disease-free survival, event-free survival (EFS), and overall survival (OS). We had four key objectives: to establish the association between pathological complete response and EFS and OS, to establish the definition of pathological complete response that correlates best with long-term outcome, to identify the breast cancer subtypes in which pathological complete response is best correlated with long-term outcome, and to assess whether an increase in frequency of pathological complete response between treatment groups predicts improved EFS and OS. We searched PubMed, Embase, and Medline for clinical trials of neoadjuvant treatment of breast cancer. To be eligible, studies had to meet three inclusion criteria: include at least 200 patients with primary breast cancer treated with preoperative chemotherapy followed by surgery; have available data for pathological complete response, EFS, and OS; and have a median follow-up of at least 3 years. We compared the three most commonly used definitions of pathological complete response--ypT0 ypN0, ypT0/is ypN0, and ypT0/is--for their association with EFS and OS in a responder analysis. We assessed the association between pathological complete response and EFS and OS in various subgroups. Finally, we did a trial-level analysis to assess whether pathological complete response could be used as a surrogate endpoint for EFS or OS. We obtained data from 12 identified international trials and 11 955 patients were included in our responder analysis. Eradication of tumour from both breast and lymph nodes (ypT0 ypN0 or ypT0/is ypN0) was better associated with improved EFS (ypT0 ypN0: hazard ratio [HR] 0·44, 95% CI 0·39-0·51; ypT0/is ypN0: 0·48, 0·43-0·54) and OS (0·36, 0·30-0·44; 0·36, 0·31-0·42) than was tumour eradication from the breast alone (ypT0/is; EFS: HR 0·60, 95% CI 0·55-0·66; OS 0·51, 0·45-0·58). We used the ypT0/is ypN0 definition for all subsequent analyses. The association between pathological complete response and long-term outcomes was strongest in patients with triple-negative breast cancer (EFS: HR 0·24, 95% CI 0·18-0·33; OS: 0·16, 0·11-0·25) and in those with HER2-positive, hormone-receptor-negative tumours who received trastuzumab (EFS: 0·15, 0·09-0·27; OS: 0·08, 0·03, 0·22). In the trial-level analysis, we recorded little association between increases in frequency of pathological complete response and EFS (R(2)=0·03, 95% CI 0·00-0·25) and OS (R(2)=0·24, 0·00-0·70). Patients who attain pathological complete response defined as ypT0 ypN0 or ypT0/is ypN0 have improved survival. The prognostic value is greatest in aggressive tumour subtypes. Our pooled analysis could not validate pathological complete response as a surrogate endpoint for improved EFS and OS. US Food and Drug Administration. Copyright © 2014 Elsevier Ltd. All rights reserved.

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Author and article information

Contributors

Yunfang Yu: yuyf9@mail.sysu.edu.cn

Chuanmiao Xie: xiechm@sysucc.org.cn

Herui Yao: yaoherui@mail.sysu.edu.cn

Journal

Journal ID (nlm-ta): Ann Surg Oncol

Journal ID (iso-abbrev): Ann Surg Oncol

Title: Annals of Surgical Oncology

Publisher: Springer International Publishing (Cham )

ISSN (Print): 1068-9265

ISSN (Electronic): 1534-4681

Publication date (Electronic): 30 June 2022

Publication date PMC-release: 30 June 2022

Publication date (Print): 2022

Volume: 29

Issue: 12

Pages: 7685-7693

Affiliations

[1 ]GRID grid.412536.7, ISNI 0000 0004 1791 7851, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Breast Tumor Centre, Phase I Clinical Trial Centre, , Sun Yat-sen Memorial Hospital, Sun Yat-sen University, ; Guangzhou, China

[2 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, Department of Nasopharyngeal Carcinoma, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, , Sun Yat-sen University Cancer Center, ; Guangzhou, Guangdong China

[3 ]GRID grid.412558.f, ISNI 0000 0004 1762 1794, Department of Medical Oncology, , The Third Affiliated Hospital of Sun Yat-sen University, ; Guangzhou, China

[4 ]GRID grid.412536.7, ISNI 0000 0004 1791 7851, Department of Radiology, , Sun Yat-sen Memorial Hospital, Sun Yat-sen University, ; Guangzhou, China

[5 ]GRID grid.469245.8, ISNI 0000 0004 1756 4881, Division of Science and Technology, , Beijing Normal University-Hong Kong Baptist University United International College, Hong Kong Baptist University, ; Zhuhai, China

[6 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, , Sun Yat-sen University Cancer Center, ; Guangzhou, China

Article

Publisher ID: 12034

DOI: 10.1245/s10434-022-12034-w

PMC ID: 9550709

PubMed ID: 35773561

SO-VID: c7559a4e-e4f2-4329-a824-f5aea7c77719

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 4 January 2022

Date accepted : 30 May 2022

Funding

Funded by: Guangzhou Science and Technology Major Program

Award ID: 201704020131

Funded by: FundRef http://dx.doi.org/10.13039/501100003453, Natural Science Foundation of Guangdong Province;

Award ID: 2017A030313828

Funded by: FundRef http://dx.doi.org/10.13039/501100007162, Guangdong Science and Technology Department;

Award ID: 2017B030314026

Award ID: 202206010078

Funded by: FundRef http://dx.doi.org/10.13039/501100018537, National Science and Technology Major Project;

Award ID: 2020ZX09201021

Funded by: Sun Yat-Sen University Clinical Research 5010 Program

Award ID: 2018007

Funded by: Sun Yat-Sen Clinical Research Cultivating Program

Award ID: SYS-C-201801

Funded by: Medical Artificial Intelligence Project of Sun Yat-Sen Memorial Hospital

Award ID: YXRGZN201902

Funded by: Guangdong Medical Science and Technology Program

Award ID: A2020558

Funded by: Tencent Charity Foundation

Award ID: SYSU-05160-20200506-0001

Award ID: SYSU-81000-20200311-0001

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 81572596

Award ID: 81972471

Award ID: 82073408

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ScienceOpen disciplines: Oncology & Radiotherapy

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A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

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Abstract

Purpose

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Conclusions

Supplementary Information

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Karger: Oncology

Most cited references 34

Cancer statistics, 2020

Computational Radiomics System to Decode the Radiographic Phenotype

Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis.

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Cited by 6

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