MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study

ConclusionsTexture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.</p

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI

Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer

BackgroundThere is a demand for additional alternative methods that can allow the differentiation of the breast tumor into molecular subtypes precisely and conveniently.PurposeThe present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to associate between the breast cancer molecular subtype and the extracted MR imaging features.MethodsWe analyzed a total of 264 patients (mean age: 47.9 ± 9.7 years; range: 19–81 years) with 264 masses (mean size: 28.6 ± 15.86 mm; range: 5–91 mm) using a Unet model and Gradient Tree Boosting for segmentation and classification.ResultsThe tumors were segmented clearly by the Unet model automatically. All the extracted features which including the shape features,the texture features of the tumors and the clinical features were input into the classifiers for classification, and the results showed that the GTB classifier is superior to other classifiers, which achieved F1-Score 0.72, AUC 0.81 and score 0.71. Analyzed the different features combinations, we founded that the texture features associated with the clinical features are the optimal features to different the breast cancer subtypes.ConclusionCAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P = 0.04 for lesions ≤ 2 cm; P = 0.02 for lesions >2 to≤ 5 cm) as with the entire data set (P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine

유방암에서 자기공명영상 근거 영상표현형과 유전자 발현 프로파일 근거 위험도의 관계

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008–0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.ope

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value &lt; 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value &lt; 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values &lt; 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value &lt; 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis

Locoregional stage assessment in clinically node negative breast cancer: Clinical, imaging, pathologic, and statistical methods

The locoregional staging remains an essential part of prognostication in breast cancer. Tumour size and biology, together with the number of lymph node metastases, guide the planning of appropriate treatments. Accurate clinical, imaging, pathologic, and statistical staging is needed as the surgical staging diminishes. In this study, 743 clinically lymph node negative breast cancer patients treated in 2009‒2017 were evaluated. Clinopathological factors were investigated in association with the number of lymph node metastases, the use of preoperative imaging methods and the surgical treatment method. A nomogram was developed and tested to predict the number of lymph node metastases after sentinel lymph node positivity. Three previously published models were validated to confirm their feasibility in the current population to predict nodal stage pN2a or pN3a. Tumour size, biologic subtype and proliferation associated with higher numbers of lymph node metastases. To predict stage pN2a or pN3a, the machine learning algorithms identified tumour size, invasive ductal histology, multifocality, lymphovascular invasion, oestrogen receptor status and the number of positive sentinel lymph nodes as risk factors. The nomograms performed well with favourable discrimination. Clinopathological factors seemed to guide preoperative magnetic resonance imaging (MRI) prior to more extensive surgery. MRI estimated the increasing tumour size more accurately than mammography or ultrasound. According to this study, clinopathological factors, additional preoperative MRI and modern statistics can be utilized in breast cancer staging without extensive surgical interference. The importance of non-surgical investigations in staging is growing in the planning of surgical, systemic and radiation treatments. Thus, maintaining the impressive survival outcomes of clinically node negative breast cancer patients can be achieved.Kliinisesti imusolmukenegatiivisen rintasyövän paikallislevinneisyyden arvioiminen. Kliiniset, kuvantamisen, patologian alan ja tilastotieteen menetelmät Kasvaimen paikallinen levinneisyys on tärkeä rintasyövän ennustetekijä. Kasvaimen koko ja biologia sekä imusolmukemetastaasien lukumäärä ohjaavat syöpähoitojen suunnittelua. Levinneisyyden selvittelyssä tarvitaan huolellista kliinistä tutkimusta sekä tarkkoja kuvantamisen, patologian alan ja tilastotieteen menetelmiä, kun kirurginen levinneisyysluokittelu vähenee. Tutkimuksessa arvioitiin vuosina 2009‒2017 hoidettujen 743 kliinisesti imusolmukenegatiivisen suomalaisen potilaan tietoja. Työssä selvitettiin kliinispatologisten tekijöiden ja kainaloimusolmukemetastaasien lukumäärän, leikkausta edeltävien kuvantamistutkimusten sekä leikkausmenetelmien yhteyttä. Ennustemalli kehitettiin ja koekäytettiin positiivisen vartijaimusolmuketutkimuksen jälkeisen imusolmukemetastaasien määrän arvioimiseksi. Kolme aiemmin julkaistua mallia validoitiin, jotta niiden käyttökelpoisuus imusolmukeluokan pN2a tai pN3a ennustamisessa varmistuisi tässä aineistossa. Kasvainkoko, biologinen alatyyppi ja jakautumisnopeus olivat yhteydessä suurempaan imusolmukemetastaasien määrään. Koneoppimisalgoritmit määrittivät levinneisyysluokan pN2a tai pN3a ennustamiseksi tarvittaviksi tekijöiksi kasvainkoon, invasiivisen duktaalisen histologian, monipesäkkeisyyden, suoni-invaasion, estrogeenireseptoristatuksen sekä positiivisten vartijaimusolmukkeiden määrän. Ennustemallit toimivat aineistossa hyvin osoittaen suotuisaa erotuskykyä. Kliinispatologiset tekijät näyttivät ohjaavan magneettikuvauspäätöstä ennen laajaa kirurgista hoitoa. Magneettikuvaus oli tarkin kuvantamismenetelmä suurenevan kasvainkoon arvioinnissa. Tämän tutkimuksen perusteella kliinispatologiset tekijät, leikkausta edeltävä täydentävä magneettikuvaus ja nykyaikaiset tilastotieteen menetelmät voivat hyödyttää rintasyövän levinneisyysluokittelua ilman laajoja kirurgisia toimenpiteitä. Kajoamattomien tutkimusten asema levinneisyysluokittelussa on vahvistumassa kirurgisten, lääkkeellisten ja sädehoitojen suunnittelun yhteydessä. Tarkka levinneisyysluokittelu edesauttaa kliinisesti imusolmukenegatiivisten rintasyöpäpotilaiden erinomaista ennustetta

Computational Imaging Biomarkers For Precision Medicine: Characterizing Heterogeneity In Breast Cancer

In the United States, 1 in 8 women are diagnosed with breast cancer. Breast tumor heterogeneity is well-established, with intratumor heterogeneity manifesting spatially and temporally. Increased heterogeneity is associated with adverse clinical outcomes. Current critical disease treatment decisions are made on the basis of biomarkers acquired from tissue samples, largely under sampling the heterogeneous disease burden. In order to drive precision medicine treatment strategies for cancer, personalized biomarkers are needed to truly characterize intratumor heterogeneity. Medical imaging can provide anon-invasive, whole tumor sampling of disease burden at the time of diagnosis and allows for longitudinal monitoring of disease progression. The studies outlined in this thesis introduce analytical tools developed through computer vision, bioinformatics, and machine learning and use diagnostic and longitudinal clinical images of breast cancer to develop computational imaging biomarkers characterizing intratumor heterogeneity. Intrinsic imaging phenotypes of spatial heterogeneity, identified in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images at the time of diagnosis, were identified and validated, demonstrating improved prognostic value over conventional histopathologic biomarkers when predicting 10-year recurrence free survival. Intrinsic phenotypes of longitudinal change in spatial heterogeneity in response to neoadjuvant treatment, identified in DCE-MRI were identified and leveraged as prognostic and predictive biomarkers, demonstrating augmented prognostic value when added to conventional histopathologic and personalized molecular biomarkers. To better characterize 4-D spatial and temporal heterogeneity, illuminated through dynamic positron emission tomography imaging, a novel 4-D segmentation algorithm was developed to identify spatially constrained, functionally discrete intratumor sub-regions. Quantifying the identified sub-regions through a novel imaging signature demonstrated the prognostic value of characterizing intratumor heterogeneity when predicting recurrence free survival, demonstrating prognostic improvement over established histopathologic biomarkers and conventional kinetic model derived parameters. Collectively, the studies in this thesis demonstrate the value of leveraging computational imaging biomarkers to characterize intratumor heterogeneity. Such biomarkers have the potential to be utilized towards precision medicine for cancer care