scholarly journals Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1491
Author(s):  
Chunli Li ◽  
Jiandong Yin
Keyword(s):  
Breast Cancer ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Factors ◽  
Clinical Value ◽  
Logistic Analysis ◽  
Diffusion Weighted ◽  
Apparent Diffusion ◽  
Her 2 ◽  
Multivariate Logistic Analysis

This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer.

scholarly journals Prognostic value of diffusion-weighted MRI for post-cardiac arrest coma

Neurology ◽  
2020 ◽  
Vol 94 (16) ◽  
pp. e1684-e1692 ◽  
Author(s):  
Karen G. Hirsch ◽  
Nancy Fischbein ◽  
Michael Mlynash ◽  
Stephanie Kemp ◽  
Roland Bammer ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Brain Tissue ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Poor Outcome ◽  
Diffusion Weighted ◽  
Blinded Study ◽  
Apparent Diffusion ◽  
Adc Values ◽  
Poor Outcomes

ObjectiveTo validate quantitative diffusion-weighted imaging (DWI) MRI thresholds that correlate with poor outcome in comatose cardiac arrest survivors, we conducted a clinician-blinded study and prospectively obtained MRIs from comatose patients after cardiac arrest.MethodsConsecutive comatose post-cardiac arrest adult patients were prospectively enrolled. MRIs obtained within 7 days after arrest were evaluated. The clinical team was blinded to the DWI MRI results and followed a prescribed prognostication algorithm. Apparent diffusion coefficient (ADC) values and thresholds differentiating good and poor outcome were analyzed. Poor outcome was defined as a Glasgow Outcome Scale score of ≤2 at 6 months after arrest.ResultsNinety-seven patients were included, and 75 patients (77%) had MRIs. In 51 patients with MRI completed by postarrest day 7, the prespecified threshold of >10% of brain tissue with an ADC <650 ×10−6 mm2/s was highly predictive for poor outcome with a sensitivity of 0.63 (95% confidence interval [CI] 0.42–0.80), a specificity of 0.96 (95% CI 0.77–0.998), and a positive predictive value (PPV) of 0.94 (95% CI 0.71–0.997). The mean whole-brain ADC was higher among patients with good outcomes. Receiver operating characteristic curve analysis showed that ADC <650 ×10−6 mm2/s had an area under the curve of 0.79 (95% CI 0.65–0.93, p < 0.001). Quantitative DWI MRI data improved prognostication of both good and poor outcomes.ConclusionsThis prospective, clinician-blinded study validates previous research showing that an ADC <650 ×10−6 mm2/s in >10% of brain tissue in an MRI obtained by postarrest day 7 is highly specific for poor outcome in comatose patients after cardiac arrest.

scholarly journals Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Alexey Surov ◽  
Yun-Woo Chang ◽  
Lihua Li ◽  
Laura Martincich ◽  
Savannah C. Partridge ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diffusion Coefficient ◽  
Triple Negative ◽  
Molecular Subtype ◽  
Luminal A ◽  
Ki 67 ◽  
Luminal B ◽  
Apparent Diffusion ◽  
Adc Values ◽  
Her 2

Abstract Background Radiological imaging plays a central role in the diagnosis of breast cancer (BC). Some studies suggest MRI techniques like diffusion weighted imaging (DWI) may provide further prognostic value by discriminating between tumors with different biologic characteristics including receptor status and molecular subtype. However, there is much contradictory reported data regarding such associations in the literature. The purpose of the present study was to provide evident data regarding relationships between quantitative apparent diffusion coefficient (ADC) values on DWI and pathologic prognostic factors in BC. Methods Data from 5 centers (661 female patients, mean age, 51.4 ± 10.5 years) were acquired. Invasive ductal carcinoma (IDC) was diagnosed in 625 patients (94.6%) and invasive lobular carcinoma in 36 cases (5.4%). Luminal A carcinomas were diagnosed in 177 patients (28.0%), luminal B carcinomas in 279 patients (44.1%), HER 2+ carcinomas in 66 cases (10.4%), and triple negative carcinomas in 111 patients (17.5%). The identified lesions were staged as T1 in 51.3%, T2 in 43.0%, T3 in 4.2%, and as T4 in 1.5% of the cases. N0 was found in 61.3%, N1 in 33.1%, N2 in 2.9%, and N3 in 2.7%. ADC values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between ADC and Ki 67 values was calculated by Spearman’s rank correlation coefficient. Results ADC values of different tumor subtypes overlapped significantly. Luminal B carcinomas had statistically significant lower ADC values compared with luminal A (p = 0.003) and HER 2+ (p = 0.007) lesions. No significant differences of ADC values were observed between luminal A, HER 2+ and triple negative tumors. There were no statistically significant differences of ADC values between different T or N stages of the tumors. Weak statistically significant correlation between ADC and Ki 67 was observed in luminal B carcinoma (r = − 0.130, p = 0.03). In luminal A, HER 2+ and triple negative tumors there were no significant correlations between ADC and Ki 67. Conclusion ADC was not able to discriminate molecular subtypes of BC, and cannot be used as a surrogate marker for disease stage or proliferation activity.

scholarly journals A Minimal lncRNA-mRNA Signature Predicts Sensitivity to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

2018 ◽  
Vol 48 (6) ◽  
pp. 2539-2548 ◽  
Author(s):  
Qian Wang ◽  
Chunmei Li ◽  
Peipei Tang ◽  
Runyuan Ji ◽  
Song Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Chemotherapy ◽  
Triple Negative Breast Cancer ◽  
Noncoding Rna ◽  
Triple Negative ◽  
Characteristic Curve ◽  
Gene Expression Signature ◽  
Receiver Operator Characteristic Curve ◽  
Response Score

Background/Aims: Triple-negative breast cancer (TNBC) is a highly aggressive malignancy that responds in a diverse manner to neoadjuvant chemotherapy (NAC). This study was aimed to uncover an RNA signature in TNBC patients which predicts pathological complete responses (pCR) to NAC by analyzing long noncoding RNA (lncRNA) and coding gene expression. Methods: Microarray datasets from 26 TNBC patients receiving NAC including ten patients showing pCR were obtained from the Gene Expression Omnibus database. Results: A total of 172 coding genes and 84 lncRNAs were differentially expressed between patients achieving pCR and those who did not. Filtering based on the predictive efficacy of response to NAC using receiver operator characteristic curve (ROC) and area under the curve (AUC) shortlisted 23 lncRNAs and 15 coding genes from consideration. Finally, a response score consisting of 1 lncRNA and 2 coding genes was developed: response score = 2.595*BPESC1 – 1.09*WDR72 –1.428*GADD45A – 0.731. The response score had good predictive performance (AUC=0.931, p< 0.01) and at the cut-off of 0.545, the response score had sensitivity and specificity of 0.8 and 0.9, respectively. Conclusion: We propose a simple gene expression signature of only three RNA species could be employed clinically to predict pCR in TNBC patients receiving NAC.

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