Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy (original) (raw)
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Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence
Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.833 versus 0.765 in an external validation cohort, p = 0.003), and can identify a subset of patients with excellent prognoses who may not need further genomic testing.
Journal of Advances in Medicine and Medical Research
Introduction: Breast cancer commonly presents in locally advanced stage (LABC) in developing countries, for which NACT followed by surgery and radiotherapy is the standard of care. There is a need for a simple tool to risk categorise patients in the clinic, so that treatment intensification can be offered to women with high risk of recurrence. Materials and Methods: Data of prospectively maintained database of LABC (between January 2007 - December 2012), who received NACT followed by surgery, radiotherapy and endocrine therapy was retrospectively analysed for clinico-pathological factors associated with disease recurrences. A recurrence risk scoring model was developed on the basis of regression coefficient of identified independent risk factors. Results: In the data set of 206 patients, the median follow-up was 48 months (range: 6-156 months) and mean and median disease-free survival (DFS) were 87.41 and 85 months. The 1, 5, 10 years DFS was 95%, 54% and 41%. The independent risk f...
PLOS ONE
Background Locoregional recurrence after neoadjuvant chemotherapy for primary breast cancer is associated with poor prognosis. It is essential to identify patients at high risk of locoregional recurrence who may benefit from extended local therapy. Here, we examined the prediction accuracy and clinical applicability of the MD Anderson Prognostic Index (MDAPI). Methods Prospective clinical data from 456 patients treated between 2003 and 2011 was analyzed. The Kaplan-Meier method was used to examine the probabilities of locoregional recurrence, local recurrence and distant metastases according to individual prognosis score, stratified by type of surgery (breast conserving therapy or mastectomy). The possible confounding of the relationship between recurrence risk and MDAPI by established risk factors was accounted for in multiple survival regression models. To define the clinical utility of the MDAPI we analyzed its performance to predict locoregional recurrence censoring patients with prior or simultaneous distant metastases. Results Mastectomized patients (42% of the patients) presented with more advanced tumor stage, lower tumor grade, hormone-receptor positive disease and consequently lower pathological complete response rates. Only a few patients presented with high-risk scores (2,7% MDAPI�3). All patients with high-risk MDAPI score (MDAPI �3) who developed locoregional recurrence were simultaneously affected by distant metastases.
Cancers
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two ob...
Predicting breast cancer recurrence
Cancer, 1982
The prognostic value of 435 cytochemical, cytometrical, morphological, epidemiological, and clinical variables was analyzed in a prospective study of 179 breast cancer patients followed for five years after mastectomy. A variable reduction was obtained by first selecting variables correlated with recurrence rate in direct (Student's 1 test) or correlation analysis with consideration of the type of variable analyzed (nominal, interval, ordinal). The 20 variables most strongly correlated with recurrence were analyzed by logistic stepwise regression analysis in order to find out what combination of variables had most discriminatory power in predicting recurrence. It was found that axillary metastization a s such was correlated with a combination of variables describing mitotic frequency, size of primary tumor and differentiation of the primary tumor (average cluster size in fine-needle biopsies). It was also found that there was a strong time dependency in the predictive power of the variables, so that different variable combinations predicted the recurrence rate during the first 2.5 year period (size of axillary metastases and primary tumor, number of lymphocytes around the tumor, mitotic frequency, and degree of differentiation) compared with the second 2.5 year period (variance of DNA content among tumor cell nuclei, number of lymphocytes around the tumor, occurrence of multiple tumors in the operated breast and occurrence of breast cancer among relatives). While other factors previously shown to be correlated with risk of recurrence were also found to be positively correlated here, they were neither as highly predictive as, nor did they increase the predictive value of the above mentioned combined variables. The current study strongly emphasizes that, at the present time, studies of recurrence prediction in human breast cancer should be based on an optimal combination of a number of variables which, independently, influence the prognosis. Further, the current study indicates that prerequisite methods for predicting breast cancer recurrence exist today.
BMC Medical Imaging
Introduction Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. Methods Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of al...
Commentary Multimodal Hazard Rate for Relapse in Breast Cancer: Quality
2016
Much has occurred since our 2010 report in Cancers. In the past few years we published several extensive reviews of our research so a brief review is all that will be provided here. We proposed in the earlier reports that most relapses in breast cancer occur within 5 years of surgery and seem to be associated with some unspecified manner of surgery-induced metastatic initiation. These events can be identified in relapse data and are correlated with clinical data. In the last few years an unexpected mechanism has become apparent. Retrospective analysis of relapse events by a Brussels anesthesiology group reported that a perioperative NSAID analgesic seems to reduce early relapses five-fold. We then proposed that primary surgery produces a transient period of systemic inflammation. This has now been identified by inflammatory markers in serum post mastectomy. That could explain the early relapses. It is possible that an inexpensive and non-toxic NSAID can reduce breast cancer relapses significantly. We want to take this opportunity to discuss database quality issues and our relapse hazard data in some detail. We also present a demonstration that the computer simulation can be calibrated with Adjuvant-on-line, an often used clinical tool for prognosis in breast cancer.
Annals of hematology & oncology, 2021
Quantitative predictions of neoadjuvant chemotherapy effects in breast cancer by individual patient data assimililation P. Castorina (a,b, * ) , D.Carco' (a) , C.Colarossi (a) , M.Mare (a,e) , L.Memeo (a) , M.Pace (b,c,d) , I.Puliafito (a) , D.Giuffrida (a)
A scoring system to predict recurrence in breast cancer patients
Surgical Oncology, 2018
Objective: Current breast cancer recurrence prediction models have limitations for clinical practice (statistical methodology, simplicity and specific populations). We therefore developed a new model that overcomes these limitations. Methods: This cohort study comprised 272 patients with breast cancer followed between 2003 and 2016. The main variable was time-to-recurrence (locoregional and/or metastasis) and secondary variables were its risk factors: age, postmenopause, grade, oestrogen receptor, progesterone receptor, c-erbB2 status, stage, multicentricity, diagnosis and treatment. A Cox model to predict recurrence was estimated with the secondary variables, and this was adapted to a points system to predict risk at 5 and 10 years from diagnosis. The model was validated internally by bootstrapping, calculating the C statistic and smooth calibration (splines). The system was integrated into a mobile application for Android. Results: Of the 272 patients with breast cancer, 47 (17.3%) developed recurrence in a mean time of 8.6±3.5 years. The system variables were: age, grade, multicentricity and stage. Validation by bootstrapping showed good discrimination and calibration. Conclusions: A points system has been developed to predict breast cancer recurrence at 5 and 10 years.
Cancers, 2021
Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the secon...