scholarly journals Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses

2021 ◽  
Vol 11 ◽  
Author(s):  
Yan Li ◽  
Zhenlu L. Yang ◽  
Wenzhi Z. Lv ◽  
Yanjin J. Qin ◽  
Caili L. Tang ◽  
...  
Keyword(s):  
High Sensitivity ◽  
Axillary Lymph ◽  
Lasso Regression ◽  
Time Intensity Curve ◽  
Combined Model ◽  
Good Discrimination ◽  
Post Contrast ◽  
Clinical Model ◽  
Dce Mri ◽  
Discriminative Models

PurposeWe aimed to assess the additional value of a radiomics-based signature for distinguishing between benign and malignant non-mass enhancement lesions (NMEs) on dynamic contrast-enhanced breast magnetic resonance imaging (breast DCE-MRI).MethodsIn this retrospective study, 232 patients with 247 histopathologically confirmed NMEs (malignant: 191; benign: 56) were enrolled from December 2017 to October 2020 as a primary cohort to develop the discriminative models. Radiomic features were extracted from one post-contrast phase (around 90s after contrast injection) of breast DCE-MRI images. The least absolute shrinkage and selection operator (LASSO) regression model was adapted to select features and construct the radiomics-based signature. Based on clinical and routine MR features, radiomics features, and combined information, three discriminative models were built using multivariable logistic regression analyses. In addition, an independent cohort of 72 patients with 72 NMEs (malignant: 50; benign: 22) was collected from November 2020 to April 2021 for the validation of the three discriminative models. Finally, the combined model was assessed using nomogram and decision curve analyses.ResultsThe routine MR model with two selected features of the time-intensity curve (TIC) type and MR-reported axillary lymph node (ALN) status showed a high sensitivity of 0.942 (95%CI, 0.906 - 0.974) and low specificity of 0.589 (95%CI, 0.464 - 0.714). The radiomics model with six selected features was significantly correlated with malignancy (P<0.001 for both primary and validation cohorts). Finally, the individual combined model, which contained factors including TIC types and radiomics signatures, showed good discrimination, with an acceptable sensitivity of 0.869 (95%CI, 0.816 to 0.916), improved specificity of 0.839 (95%CI, 0.750 to 0.929). The nomogram was applied to the validation cohort, reaching good discrimination, with a sensitivity of 0.820 (95%CI, 0.700 to 0.920), specificity of 0.864 (95%CI,0.682 to 1.000). The combined model was clinically helpful, as demonstrated by decision curve analysis.ConclusionsOur study added radiomics signatures into a conventional clinical model and developed a radiomics nomogram including radiomics signatures and TIC types. This radiomics model could be used to differentiate benign from malignant NMEs in patients with suspicious lesions on breast MRI.

scholarly journals Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247074
Author(s):  
Hong-Bing Luo ◽  
Yuan-Yuan Liu ◽  
Chun-hua Wang ◽  
Hao-Miao Qing ◽  
Min Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Nodes ◽  
Diagnostic Performance ◽  
Pharmacokinetic Model ◽  
Image Features ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Combined Model ◽  
Dce Mri ◽  
Diagnostic Models

Objective To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer. Materials and methods A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models. Results The 106 radiomic features were reduced to 4 ALNM diagnosis–related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947–0.997]) in the training cohort and 0.979 (95% CI [0.952–1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05). Conclusion Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.

Radiomics Analysis Based on CT’s greater-omental caking for Predicting Pathological Grading of Pseudomyxoma Peritonei

2021 ◽  
Author(s):  
Nan Zhou ◽  
Ruixue Dou ◽  
Xichao Zhai ◽  
Jingyang Fang ◽  
Jiajun Wang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Pseudomyxoma Peritonei ◽  
Diagnostic Efficiency ◽  
Combined Model ◽  
Clinical Model ◽  
Model Based ◽  
Preoperative Serum ◽  
Imaging Observation ◽  
Omental Caking ◽  
The Impact

Abstract Purpose: The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater mental caking as the imaging observation index, a clinical model including clinical indexes, and a combination model of these two.Methods: A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and joint model) were used to predict PMP pathological grading. The models’ diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP’s different pathological grades was evaluated.Results: The results showed that the radiomics model based on the CT’s greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power n for determining PMP pathological grade.Conclusion: The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grade, which is important in the treatment selection method and prognosis assessment.

scholarly journals Radiomics Nomogram of DCE-MRI for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

2020 ◽  
Vol 10 ◽  
Author(s):  
Ning Mao ◽  
Yi Dai ◽  
Fan Lin ◽  
Heng Ma ◽  
Shaofeng Duan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Axillary Lymph Node ◽  
Roc Curves ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Dce Mri ◽  
Validation Set ◽  
Selection Operator ◽  
Radiomics Signature

PurposeThis study aimed to establish and validate a radiomics nomogram based on dynamic contrast-enhanced (DCE)-MRI for predicting axillary lymph node (ALN) metastasis in breast cancer.MethodThis retrospective study included 296 patients with breast cancer who underwent DCE-MRI examinations between July 2017 and June 2018. A total of 396 radiomics features were extracted from primary tumor. In addition, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the features. Radiomics signature and independent risk factors were incorporated to build a radiomics nomogram model. Calibration and receiver operator characteristic (ROC) curves were used to confirm the performance of the nomogram in the training and validation sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA).ResultsThe radiomics signature consisted of three ALN-status-related features, and the nomogram model included the radiomics signature and the MR-reported lymph node (LN) status. The model showed good calibration and discrimination with areas under the ROC curve (AUC) of 0.92 [95% confidence interval (CI), 0.87–0.97] in the training set and 0.90 (95% CI, 0.85–0.95) in the validation set. In the MR-reported LN-negative (cN0) subgroup, the nomogram model also exhibited favorable discriminatory ability (AUC, 0.79; 95% CI, 0.70–0.87). DCA findings indicated that the nomogram model was clinically useful.ConclusionsThe MRI-based radiomics nomogram model could be used to preoperatively predict the ALN metastasis of breast cancer.

scholarly journals Whole-Body Functional MRI and PET/MRI in Multiple Myeloma

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3155
Author(s):  
Sébastien Mulé ◽  
Edouard Reizine ◽  
Paul Blanc-Durand ◽  
Laurence Baranes ◽  
Pierre Zerbib ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
Functional Mri ◽  
Bone Marrow Involvement ◽  
High Sensitivity ◽  
Response To Treatment ◽  
Whole Body ◽  
Response Evaluation ◽  
Whole Body Mri ◽  
Dce Mri

Bone disease is one of the major features of multiple myeloma (MM), and imaging has a pivotal role in both diagnosis and follow-up. Whole-body magnetic resonance imaging (MRI) is recognized as the gold standard for the detection of bone marrow involvement, owing to its high sensitivity. The use of functional MRI sequences further improved the performances of whole-body MRI in the setting of MM. Whole-body diffusion-weighted (DW) MRI is the most attractive functional technique and its systematic implementation in general clinical practice is now recommended by the International Myeloma Working Group. Whole-body dynamic contrast-enhanced (DCE) MRI might provide further information on lesions vascularity and help evaluate response to treatment. Whole Body PET/MRI is an emerging hybrid imaging technique that offers the opportunity to combine information on morphology, fat content of bone marrow, bone marrow cellularity and vascularization, and metabolic activity. Whole-body PET/MRI allows a one-stop-shop examination, including the most sensitive technique for detecting bone marrow involvement, and the most recognized technique for treatment response evaluation. This review aims at providing an overview on the value of whole-body MRI, including DW and DCE MRI, and combined whole-body 18F-FDG PET/MRI in diagnosis, staging, and response evaluation in patients with MM.

scholarly journals Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Meijie Liu ◽  
Ning Mao ◽  
Heng Ma ◽  
Jianjun Dong ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Regression Method ◽  
Pharmacokinetic Parameters ◽  
Combined Model ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Preoperative Prediction ◽  
Contrast Enhanced

Abstract Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

2012 ◽  
Author(s):  
Ahmed B. Ashraf ◽  
Lilie Lin ◽  
Sara C. Gavenonis ◽  
Carolyn Mies ◽  
Eric Xanthopoulos ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Dce Mri ◽  
Node Metastasis

Pharmacokinetic (PK) and pharmacodynamic (PD) study of the effect of imatinib (Ima) on tumor (T) vascularity (V) and on gemcitabine (Ge) uptake (U) using non-invasive dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) and spectroscopy (MRS)—Study BUS228

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 12006-12006
Author(s):  
W. Wolf ◽  
C. A. Presant ◽  
V. Waluch ◽  
E. J. Chen
Keyword(s):  
Accumulation Rate ◽  
Phase 1 ◽  
Fluid Pressure ◽  
Post Contrast ◽  
Dce Mri ◽  
Chemotherapy Drugs ◽  
Non Invasive ◽  
Contrast Enhanced ◽  
Magnetic Resonance Imaging Mri ◽  
Moderate Nausea

12006 Background: Ima has been reported to increase T U of other chemotherapy drugs and to reduce interstitial fluid pressure (IFP) in experimental animals. Poplin et al performed a phase 1 analysis of Ima plus Ge in solid tumors (AACR 95:405, 2004). We tested ImaGe to determine the T PK and PD effects of Ima, Ge, and the ImaGe combination using DCE-MRI and MRS. Methods: Patients (pt) with measurable and MRI-imagible refractory solid T possibly responsive to Ge were randomized to receive either: one course of Ima with PK/PD, followed by one course of Ge with PK/PD, followed by the combination ImaGe; or one course of Ge with PK/PD followed by one course of Ima with PK/PD, followed by ImaGe. Ge was given at 900 mg/m2 IV over 30 min. for PK/PD and at 10 mg/m2/min. for continued therapy. Ima was given at 400 mg daily for 5 d. with Ge given on day 3. Doses were adjusted for toxicity. T V was measured by the use of DCE-MRI, as described previously (AACR 95:490,2004), where the initial contrast accumulation rate (ICAR) was calculated as the slope of the influx curve, and the delayed contrast accumulation rate (DCAR), measured between 2–20 min post contrast administration, as an approximation of IFP. Ge U was measured by serial 19F-MRS for ∼ 1hr post Ge administration. Results: To date 7 pts have been evaluated for the trial. Two pts have entered the trial and completed one cycle of therapy for PK/PD evaluation. Ima produced moderate nausea in both pts. Other toxicity was negligible. In the first pt Ima produced an 18% increase in the ICAR and a 72% increase in the DCAR but there was no significant change observed in the Ge uptake. In the second pt, Ima produced a 60% increase in the ICAR and a 21% increase in the DCAR. Neither of the 2 pts responded to treatment. Further pts are under study and their PK/PD results will be presented. Conclusions: PK and PD can be measured using DCE MRI together with MRS to determine the clinical affects of Ima, Ge, and the Ima-Ge combination. Current results indicate that Ima has a measurable effect on T V, but its relation to drug U and pt response require further pt evaluations to be definitive. [Table: see text]

Dynamic contrast-enhanced MRI to predict intratumoral molecular heterogeneity in clear cell renal cell carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 4580-4580
Author(s):  
Durga Udayakumar ◽  
Ze Zhang ◽  
Durgesh Dwivedi ◽  
Yin Xi ◽  
Tao Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Molecular Heterogeneity ◽  
Cell Renal Cell Carcinoma ◽  
Post Contrast ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

4580 Background: Mutation/inactivation of VHL in clear cell renal cell carcinoma (ccRCC) leads to upregulation of hypoxia inducible factors ( HIFs) and angiogenesis. However, ccRCC is characterized by high intra-tumor heterogeneity (ITH). Random small samples such as those in percutaneous biopsies are likely limited for characterization of molecular alterations in heterogeneous ccRCCs. We hypothesize that whole-tumor dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) is useful to noninvasively identify ITH in ccRCC. Methods: This IRB-approved, prospective, HIPAA-compliant study, included 62 ccRCCs. 3T DCE MRI was obtained prior to nephrectomy. Surgical specimens were sectioned to match MRI acquisition plane. 182 snap frozen samples (49 tumors) and adjacent uninvolved renal parenchyma (URP) were collected. RNA isolations, cDNA library preparation and mRNA sequencing were performed using standard protocols. RNA expression in 81 tumor samples were correlated (Spearman ranked) with % enhancement in a region of interest (ROI) drawn in the same location of the tumor on pre- and 3 different post-contrast DCE MRI phases. Gene function overrepresentation (OR) analyses were done on top positively and negatively correlated genes. False discovery rate (FDR) < 0.1 was considered statistically significant. Results: Principal component analysis of > 20,000 genes indicated distinct gene expression in tumors from URP. Unsupervised clustering showed enrichment of ccA samples (better prognosis) compared to ccB samples (worse prognosis). Importantly, ccA and ccB samples coexisted in 25% of tumors. DCE-MRI % enhancement correlated with expression of > 300 genes (p < 0.003, FDR < 0.1). OR analyses placed angiogenic pathway gene processes and the immune/inflammatory response processes within the top 5 positively- and negatively-correlated gene functions, respectively. HIF2 target genes correlated positively with % enhancement. Conclusions: DCE MRI detects specific molecular signatures and may help overcome the challenges of ITH in ccRCC. Further research is needed to explore the potential role of DCE MRI to assess response to antiangiogenic and immune-based therapies.

Progressive massive fibrosis in patients with pneumoconiosis: utility of MRI in differentiating from lung cancer

2017 ◽  
Vol 59 (1) ◽  
pp. 72-80 ◽  
Author(s):  
Yukihiro Ogihara ◽  
Kazuto Ashizawa ◽  
Hideyuki Hayashi ◽  
Takeshi Nagayasu ◽  
Tomayoshi Hayashi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Enhancement Pattern ◽  
Time Intensity Curve ◽  
Post Contrast ◽  
Mri Features ◽  
Imaging Sequence ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Study ◽  
Pattern Sensitivity ◽  
Progressive Massive Fibrosis

Background It is occasionally difficult to distinguish progressive massive fibrosis (PMF) from lung cancer on computed tomography (CT) in patients with pneumoconiosis. Purpose To evaluate the magnetic resonance imaging (MRI) features of PMF and to assess its ability to differentiate PMF from lung cancer. Material and Methods Between 2000 and 2014, 40 pulmonary lesions suspected to be lung cancer on the basis of CT in 28 patients with known pneumoconiosis were evaluated. Twenty-four of the 40 lesions were pathologically or clinically diagnosed as PMF. The signal pattern on T2-weighted (T2W) images, post-contrast enhancement pattern on T1-weighted (T1W) images, and the pattern of the time intensity curve (TIC) on contrast-enhanced dynamic studies were evaluated. All images were analyzed independently by two chest radiologists. Results All 24 PMF lesions showed low signal intensity (SI) on T2W images (sensitivity, 100%), while 15 of 16 lung cancer lesions showed intermediate or high SI on T2W images (specificity, 94%) when PMF was regarded as a positive result. Six of 17 PMF lesions showed a homogeneous enhancement pattern (sensitivity, 35%), and 4/9 lung cancer lesions showed an inhomogeneous or a ring-like enhancement pattern (specificity, 44%). Six of 16 PMF lesions showed a gradually increasing enhancement pattern (sensitivity, 38%), and 7/9 lung cancer lesions showed rapid enhancement pattern (specificity, 78%). Conclusion When differentiation between PMF and lung cancer in patients with pneumoconiosis is difficult on CT, an additional MRI study, particularly the T2W imaging sequence, may help differentiate between the two.

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