Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer

To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer. Three hundred four consecutive patients from center I with pathologically diagnosed rectal adenocarcinoma (training dataset, n = 213; internal validation dataset, n = 91) were enrolled in our retrospective study. The patients from center II (n = 86) were selected as an external validation dataset. A total of 960 imaging features were extracted from high-resolution T2W images for each patient. Five steps, mainly univariate statistical tests, were applied for feature selection. Subsequently, three classification methods, i.e., logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithm, were applied to develop the radiomics signature for KRAS prediction in the training dataset. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Seven radiomics features were screened as a KRAS-associated radiomics signature of rectal cancer. Our best prediction model was obtained with SVM classifiers with AUC of 0.722 (95%CI, 0.654–0.790) in the training dataset. This was validated in the internal and external validation datasets with good calibration, and the corresponding AUCs were 0.682 (95% CI, 0.569–0.794) and 0.714 (95% CI, 0.602–0.827), respectively. DCA confirmed its clinical usefulness. The proposed T2WI-based radiomics signature has a moderate performance to predict KRAS status, and may be useful for supplementing genomic analysis to determine KRAS expression in rectal cancer patients. • T2WI-based radiomics showed a moderate diagnostic significance for KRAS status. • The best prediction model was obtained with SVM classifier. • The baseline clinical and histopathological characteristics were not associated with KRAS mutation.     

基于T2WI和增强MRI影像组学列线图预测宫颈鳞癌淋巴脉管间隙浸润

目的:探讨基于T₂WI和增强MRI影像组学列线图对宫颈鳞癌淋巴脉管间隙浸润(LVSI)的预测价值。方法:将92例经术后病理证实的宫颈鳞癌患者纳入研究,并按7:3的比例随机分为训练集(66例)和验证集(26例)。所有患者术前行MRI检查,在横轴面T₂WI和对比增强T₁WI(T1CE)上选取病灶最大层面沿肿瘤边缘勾画ROI,应用AK软件提取影像组学特征。采用mRMR和LASSO回归分析对提取的纹理特征进行初步筛选,然后进行多因素logistic回归分析,构建影像组学模型。使用单因素logistic回归分析筛选临床病理危险因素,并使用多因素logistic回归结合影像组学评分(Radscore)构建影像组学列线图。应用ROC曲线评估影像组学模型、临床病理危险因素模型和影像组学列线图模型的预测能力,并应用决策曲线分析评估影像组学列线图的临床应用价值。结果:在T₂WI和T1CE图像上分别提取病灶的396个影像组学特征,最终筛选出14个具有最大诊断效能的纹理特征。使用多因素logistic回归构建包含FIGO分期、分化程度和Radscore的影像组学列线图。影像组学列线图的预测效能优于临床病理危险因素模型(训练集中,AUC:0.96 vs.0.70;Delong检验:Z=4.04,P=5.415e-05;验证集中,AUC:0.87 vs.0.71;delong检验:Z=1.24,P=0.02)。决策曲线分析显示风险阈值为0.01~1.00时使用影像组学列线图对预测宫颈鳞癌LVSI情况的临床应用价值较大。结论:基于双序列MRI构建的影像组学列线图对宫颈鳞癌LVSI情况有较好的预测能力,可作为一种术前评估的无创性影像学生物标志。     

基于MRI征象及影像组学的列线图预测腮腺良性与恶性肿瘤的价值

目的建立并验证基于MRI征象和影像组学的列线图鉴别腮腺良性与恶性肿瘤的效能。方法回顾性收集2015年1月至2020年5月青岛大学附属医院86例经手术病理证实的腮腺肿瘤患者为训练集,收集2013年1月至2020年1月香港大学深圳医院35例患者为独立外部验证集。采用logistic回归基于临床及MRI征象建立临床诊断模型。基于术前平扫T1WI和预饱和脂肪抑制T2WI(fs-T2WI)进行影像组学特征提取,建立影像组学诊断模型。基于影像组学评分及临床诊断模型,通过logistic回归建立影像组学+临床联合诊断模型及列线图。采用受试者操作特征(ROC)曲线评价各模型诊断腮腺良性与恶性肿瘤的效能,ROC曲线下面积(AUC)的比较采用DeLong检验。结果Logistic回归结果显示,腮腺深叶受累(OR值为3.285,P=0.040)和周围组织结构侵犯(OR值为15.919,P=0.013)是腮腺恶性肿瘤的独立影响因素,将二者构建临床诊断模型。基于平扫T1WI和fs-T2WI,共提取19个特征构建影像组学诊断模型。联合影像组学评分以及腮腺深叶受累、周围组织结构侵犯2个常规影像学特征建立联合诊断模型及列线图。临床诊断模型、影像组学诊断模型、联合诊断模型在训练集和验证集中诊断腮腺良性与恶性肿瘤的AUC分别为0.758、0.951、0.953和0.752、0.941、0.964。在训练集和验证集中,影像组学诊断模型、联合诊断模型的AUC均高于临床诊断模型(训练集:Z=3.95、4.31,P均<0.001;验证集:Z=2.16、2.67,P=0.031、0.008),影像组学诊断模型、联合诊断模型间AUC差异无统计学意义(训练集:Z=0.39,P=0.697;验证集:Z=1.10,P=0.273)。结论本研究所建立的MRI影像组学模型以及由腮腺深叶受累、周围组织结构侵犯、MRI影像组学特征组成的联合诊断模型,能有效鉴别腮腺良恶性肿瘤,具有较高的预测效能。     

基于不同时相增强CT的影像组学对胰腺实性假乳头状肿瘤侵袭性行为的预测价值

目的:探讨CT影像组学对胰腺实性假乳头状肿瘤（pSPN）侵袭性行为的预测价值。方法:回顾性分析2012年1月至2021年1月郑州大学第一附属医院经术后病理证实的pSPN患者的CT图像,其中侵袭性23例、非侵袭性59例。分别在平扫、动脉期和静脉期CT图像上逐层勾画感兴趣区（ROI）获得三维ROI,每个ROI提取1 316...     

基于乳腺X线图像影像组学列线图对乳腺癌腋窝淋巴结转移的预测价值

目的:探讨基于乳腺X线图像影像组学列线图对乳腺癌腋窝淋巴结(ALN)转移的预测价值.方法:回顾性分析188例乳腺癌患者的乳腺X线图像和临床资料,按照7:3的比例将患者随机分割为训练组(n=130)和验证组(n=58).使用MaZda软件在乳腺X线图像内提取影像组学特征,应用方差选择法和最小绝对收缩与选择算子算法(LAS...     

Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface

Purpose

To evaluate the potential of subregional radiomics as a novel tumor marker in predicting epidermal growth factor receptor (EGFR) mutation status and response to EGFR-tyrosine kinase inhibitor (TKI) therapy in NSCLC patients with brain metastasis (BM).

Materials and methods

We included 230 patients from center 1, and 80 patients were included from center 2 to form a primary and external validation cohort, respectively. Patients underwent contrast-enhanced T1-weighted and T2-weighted MRI scans before treatment. The individual- and population-level clustering was used to partition the peritumoral edema area (POA) into phenotypically consistent subregions. Radiomics features were calculated and selected from the tumor active area (TAA), POA and subregions, and used to develop models. Prediction values of each region were investigated and compared with receiver operating characteristic curves and Delong test.

Results

For predicting EGFR mutations, a multi-region combined model (EGFR-Fusion) was developed based on joint of the partitioned metastasis/brain parenchyma (M/BP)-interface and TAA, and generated the highest prediction performance in the training (AUC?=?0.945, SEN?=?0.878, SPE?=?0.937), internal validation (AUC?=?0.880, SEN?=?0.733, SPE?=?0.969), and external validation (AUC?=?0.895, SEN?=?0.875, SPE?=?0.800) cohorts. For predicting response to EGFR-TKI, the developed multi-region combined model (TKI-Fusion) yielded predictive AUCs of 0.869 (SEN?=?0.717, SPE?=?0.884), 0.786 (SEN?=?0.708, SPE?=?0.818), and 0.802 (SEN?=?0.750, SPE?=?0.800) in the training, internal validation and external validation cohort, respectively.

Conclusion

Our study revealed that complementary information regarding the EGFR status and response to EGFR-TKI can be provided by subregional radiomics. The proposed radiomics models may be new markers to guide treatment plans for NSCLC patients with BM.

     

CT影像组学模型预测肝移植术后肝癌早期复发的价值

 目的 探索基于CT影像组学技术构建的模型在预测肝细胞癌患者肝移植术后早期复发的价值。方法 回顾性分析接受肝移植治疗的131例肝癌患者,随机分为训练组(92例)和验证组(39例),术后定期随访,了解是否发生早期复发。通过逐层勾画肿瘤边缘对肿瘤进行三维分割并进行特征提取,共提取1218个影像组学特征。具有潜在预测价值特征的筛选选用LASSO算法。基于筛选出的特征,logistic回归应用于肝移植术后预测模型的构建。通过曲线下面积(area under the curve, AUC)对模型预测患者是否会早期复发的效能进行评价。结果 筛选出8个具有潜在预测价值的特征,预测模型在训练组中AUC为0.828,敏感度、特异度分别为82.4%、74.7%;在验证组中AUC为0.856,敏感度、特异度分别为77.8%、86.7%。结论 术前增强CT影像组学技术构建的模型,对预测肝癌肝移植术后复发具有一定价值。     

超声影像组学标签预测三阴性乳腺癌的价值

目的 探讨基于常规超声的影像组学标签在术前诊断三阴性乳腺癌(TNBC)的价值.资料与方法 回顾性连续收集230例经手术病理证实的肿块型浸润性乳腺癌患者的临床资料和术前超声图像,按照1:2随机抽样选取TNBC与非TNBC共102例患者纳入本研究.按超声检查时间顺序,将患者分为训练组66例和验证组36例.通过ImageJ软...     

Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection

To identify a CT-based radiomics nomogram for survival prediction in patients with resected pancreatic ductal adenocarcinoma (PDAC). A total of 220 patients (training cohort n = 147; validation cohort n = 73) with PDAC were enrolled. A total of 300 radiomics features were extracted from CT images. And the least absolute shrinkage and selection operator algorithm were applied to select features and develop a radiomics score (Rad-score). The radiomics nomogram was constructed by multivariate regression analysis. Nomogram discrimination, calibration, and clinical usefulness were evaluated. The association of the Rad-score and recurrence pattern in PDAC was evaluated. The Rad-score was significantly associated with PDAC patient’s disease-free survival (DFS) and overall survival (OS) (both p < 0.001 in two cohorts). Incorporating the Rad-score into the radiomics nomogram resulted in better performance of the survival prediction than that of the clinical model and TNM staging system. In addition, the radiomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. There was no association between the Rad-score and recurrence pattern. The radiomics nomogram integrating the Rad-score and clinical data provided better prognostic prediction in resected PDAC patients, which may hold great potential for guiding personalized care for these patients. The Rad-score was not a predictor of the recurrence pattern in resected PDAC patients. • The Rad-score developed by CT radiomics features was significantly associated with PDAC patients’ prognosis. • The radiomics nomogram integrating the Rad-score and clinical data has value to permit non-invasive, low-cost, and personalized evaluation of prognosis in PDAC patients. • The radiomics nomogram outperformed clinical model and the TNM staging system in terms of survival estimation.     

A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning

Objective:To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning.Methods:Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated.Results:By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92.Conclusion:The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control.Advances in knowledge:For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.     

基于MRI影像组学特征构建膀胱尿路上皮癌病理分级预测模型的价值

目的 探讨基于MRI影像组学特征构建膀胱尿路上皮癌病理分级预测模型的价值.方法 搜集经手术病理证实的100例膀胱尿路上皮癌患者,其中低级别尿路上皮癌(LGUC)28例和高级别尿路上皮癌(HGUC) 72例.通过随机分层抽样方法以7∶3的比例分为训练组及测试组.使用ITK-SNAP软件勾画T2WI、扩散加权成像(DWI)...     

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

To create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa). This study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3–5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets. The best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980–0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920–0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution. The phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa. • T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance. • Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest. • The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.     

MRI影像组学对早期口腔舌鳞状细胞癌隐匿性颈淋巴结转移的预测价值

目的:探讨基于术前常规MRI的影像组学对早期口腔舌鳞状细胞癌（OTSCC）隐匿性颈淋巴结转移的预测价值。方法:回顾性收集2015年1月至2019年12月上海交通大学医学院附属第九人民医院77例早期OTSCC患者（临床分期cT1~2N0M0）的术前MRI数据。所有患者均行原发灶切除及选择性颈淋巴结清扫术,且具有术后淋巴结...     

基于MRI影像组学模型对食管癌淋巴结转移的预测价值

目的 探讨基于T₂WI及增强T₁WI序列MRI影像组学特征构建模型预测食管癌淋巴结转移的价值。 方法 回顾性收集经病理证实并行多模态MRI检查的食管癌病人120例，男89例，女31例，平均年龄（63.4±8.2）岁。将病人按7:3比例随机分为训练集84例和验证集36例。以手术病理为金标准将病人分为淋巴结转移阴性组（56例）和阳性组（64例）。采用A.K.软件基于T₂WI和增强T₁WI获取肿瘤兴趣区体积（VOI），提取影像组学特征并进行降维筛选，并采用Logistic回归分析法构建基于T₂WI、增强T₁WI、联合T₂WI+增强T₁WI序列的影像组学模型。2组间一般临床资料比较采用独立样本t检验和χ²检验。采用组内相关系数（ICC）分析2名医师获取VOI的一致性。采用受试者操作特征（ROC）曲线评估预测模型的诊断效能，计算其曲线下面积（AUC），并采用DeLong法比较不同模型的AUC值。 结果 淋巴结转移阴性和阳性组间病人的性别、年龄，肿瘤位置、病理类型及肿瘤长度的差异均无统计学意义（均P>0.05）。2名医师在T₂WI和增强T₁WI影像上获取VOI的一致性均较好（均P>0.8）。经筛选后，基于T₂WI、增强T₁WI、T₂WI+增强T₁WI联合序列获得的影像组学特征分别有5、6、9个。在训练集及验证集中联合模型的AUC高于增强T₁WI和T₂WI模型，且增强T₁WI模型的AUC高于T₂WI模型（均P<0.05）。 结论 基于MRI影像组学特征构建的模型对食管癌病人术前淋巴结转移具有良好的预测效能，且T₂WI+增强T₁WI联合模型较单序列模型的预测价值更高。     

基于MRI影像组学鉴别卵巢卵泡膜细胞瘤与阔韧带肌瘤

目的 探讨基于MRI影像组学对卵巢卵泡膜细胞瘤（OTCA）与阔韧带肌瘤（BLM）的鉴别诊断价值。资料与方法 回顾性分析安阳市肿瘤医院2016年1月—2021年3月经病理证实的76例OTCA和58例BLM的MRI图像,比较两组疾病的MRI特征。于肿瘤最大层面勾画感兴趣区提取T2WI脂肪抑制序列图像纹理特征,采用分层抽样方式按照7∶3分为训练组104例和测试组30例,根据病理结果分为OTCA亚组和BLM亚组。基于训练组,使用最小绝对收缩和选择算子回归分析筛选关键特征,根据回归模型中变量的回归系数,建立线性方程计算影像组学标签评分。采用受试者工作特征（ROC）曲线评价基于MRI图像特征、影像组学及其组合区分两种疾病的能力。结果 共4个MRI特征为鉴别两组疾病的独立特征,包括同侧卵巢可见性（χ2=5.503,P<0.05）、外周囊性区（χ2=7.693,P<0.05）、动脉期强化程度（P<0.05）及表观扩散系数（t=3.310,P<0.05）;训练组和测试组OTCA、BLM亚组的影像组学标签评分比较,差异均有统计学意义（P<0.05）。联合MRI图像特征和影像组...     

基于磁共振表观扩散系数图的影像组学模型对前列腺癌Gleason分级的诊断价值

目的:探讨基于磁共振ADC图的影像组学模型对诊断前列腺癌侵袭度的价值。方法:回顾性分析2018年1月至2019年5月在建湖医院,经手术病理证实且能确定Gleason分级的42例患者的ADC图像,将癌灶分为高危组(Gleason评分≥8)和低中危组(Gleason评分≤7)2组。其中中低危21例、高危21例。应用ITK-SNAP软件勾化感兴趣区(ROI),将ADC图像导人Analysis-Kinetics分析软件,进行影像特征提取。采用Lasso回归分析进行特征降维。通过LASSO降维筛选出的特征和相应加权系数乘积的线性组合来建立鉴别中低危、高危前列腺癌的模型,绘制ROC曲线评价模型鉴别中低危、高危前列腺癌的预测效能。结果:共提取396个影像组学特征,通过特征筛选后最后筛选出7个影像组学特征。建模后影像组学特征对鉴别中低危、高危前列腺癌具有较好的预测效能,预测模型在训练组中鉴别效能的曲线下面积、准确度、敏感度、特异度、阳性预测值和阴性预测值分别为0.97、93.3%、93.3%、93.3%、0.93和0.93;在验证组中的曲线下面积、准确度、敏感度、特异度、阳性预测值和阴性预测值分别为0.97、91.7%、83.3%、100.0%、1和0.86;结论:基于磁共振ADC图的影像组学模型对前列腺癌Gleason分级具有诊断价值。     

基于动态增强MRI影像组学评分和激素受体的列线图预测乳腺癌新辅助化疗不敏感的价值

目的 探讨基于动态增强MRI（DCE-MRI）影像组学评分（Radscore）和激素受体状态的列线图预测乳腺癌新辅助化疗（NAC）疗效不敏感的价值。 方法 回顾性收集128例行乳腺癌NAC治疗的女性病人,平均年龄（49.2±10.0）岁。128例病人按照7∶3比例随机分为训练集90例（疗效敏感者47例,疗效不敏感者43例）和测试集38例（疗效敏感者15例,疗效不敏感者23例）。基于DCE-MRI影像提取并筛选影像组学特征,采用多因素逻辑回归构建影像组学模型并计算模型的Radscore。采用t检验、χ²检验或Fisher确切概率检验比较训练集和测试集中临床病理指标[年龄、雌激素受体（ER）、孕激素受体（PR）、人表皮生长因子受体-2（HER-2）和肿瘤增殖细胞核抗原-67（Ki-67）],将差异有统计学意义的临床病理指标和Radscore纳入多因素逻辑回归,建立联合模型和列线图。应用受试者操作特征（ROC）曲线下面积（AUC）评价影像组学模型和联合模型的预测效能。应用决策曲线评估影像组学模型和联合模型的临床应用价值。 结果 在训练集中,ER和PR在疗效敏感与不敏感组间的差异均有统计学意义（均P<0.05）,但未得到测试集的验证（均P>0.05）。在训练集中,联合模型预测NAC不敏感的AUC值和准确度分别高于影像组学模型约3.8%和3.1%。在测试集中,联合模型预测NAC不敏感的AUC值高于影像组学模型,其较后者提高了约2.3%,但两者的准确度相同。在基于ER、PR和Radscore构建的联合模型列线图中,Radscore得分最高,其次是ER和PR。决策曲线分析显示联合模型的临床获益高于影像组学模型。 结论 基于DCE-MRI的Radscore和ER、PR构建的联合模型列线图能够较好地预测NAC疗效不敏感。     

基于薄层CT的影像组学和形态学特征联合模型在预测磨玻璃样肺腺癌浸润程度的价值

目的 探讨基于薄层CT的影像组学和形态学特征联合模型在预测磨玻璃样肺腺癌中的原位癌(AIS)、微浸润腺癌(MIA)、浸润性腺癌(IAC)的价值.方法 回顾性分析2018年6月至2021月3月经病理证实的327例肺腺癌患者(335个病灶)术前肺部CT检查图像,随机选取201个为训练集,134个为测试集.肺腺癌参照病理金标...     

CT影像组学对中轴骨脊索瘤与骨巨细胞瘤的鉴别诊断价值

目的:建立术前鉴别中轴骨脊索瘤与骨巨细胞瘤的影像组学模型,并验证其诊断效能.方法:回顾性纳入中轴骨脊索瘤59例、骨巨细胞瘤33例共92例患者,64例为训练集,28例为验证集.基于CT图像进行影像组学特征提取,采用LASSO模型进行特征选择,构建影像组学模型,并计算影像组学得分(Rad-score).通过Logistic...     

Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer

Objectives:To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers.Methods:152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller–Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors.Results:20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85–0.96, 0.86–0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86–1.00, 0.82–1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64–0.84, 0.46–0.82) in terms of NAC sensitivity and pCR.Conclusions:The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens.Advances in knowledge:Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity.