Multistatic imaging is corrected to near-field scattering imaging with amplitude and phase compensation. Simulated results using this implementation for 2D conducting cylinder is provided with frequency diversity, together with the transmitter view angle diversity.
The rarity of metaplastic breast carcinoma (MBC) has resulted in limited sonographic data. Given the inferior prognosis of MBC compared to invasive ductal carcinoma (IDC), accurate preoperative differentiation between the two is imperative for effective treatment planning and prognostic prediction. The objective of this study was to assess the diagnostic accuracy of MBC and differentiate it from IDC by analyzing sonographic and clinicopathologic features.
Background: Lesion-to-brain contrast after gadolinium administration is significantly higher at 3.0 Tesla (T) compared to 1.5T. The high in vivo relaxivity of gadobenate dimeglumine (Gd-BOPTA) may permit the use of lower-dose contrast agents. Purpose: To investigate whether low-dose contrast-enhanced MRI at 3.0T using a high-relaxivity contrast agent (Gd-BOPTA) can achieve a comparable or improved contrast-to-noise ratio (CNR) for the detection of brain metastases compared with examination of the same patient at 1.5T using a standard dose of gadopentetate dimeglumine (Gd-DTPA). Material and Methods: A total of 18 patients with known brain metastases were first imaged at 1.5T with 0.1 mmol/kg Gd-DTPA. Patients returned at least 24 hours later for imaging at 3.0T with Gd-BOPTA at cumulative doses of 0.025 mmol/kg, 0.05 mmol/kg, 0.075 mmol/kg, and 0.1 mmol/kg (0.1 mmol/kg body weight overall). The CNR of enhancing brain lesions compared to the normal contralateral white matter was calculated. For the 3.0T study using different cumulative doses of Gd-BOPTA, the CNR of lesions was compared with CNR of the same lesions imaged at 1.5T using 0.1 mmol/kg Gd-DTPA, by using the Wilcoxon matched-pairs signed-rank test. Results: At 1.5T with 0.1 mmol/kg Gd-DTPA, the mean CNR between enhanced lesions and cerebral white matter was 12.01 ± 2.53. With 3.0T imaging using different cumulative doses of Gd-BOPTA, the mean CNRs were 7.19 ± 4.06, 15.31 ± 6.37, 25.44 ± 11.02, and 31.88 ± 13.21. At 3.0T with 0.05 mmol/kg Gd-BOPTA, CNR was 1.34-fold higher compared to CNR at 1.5T with 0.1 mmol/kg Gd-DTPA ( P <0.01). Conclusion: Comparable contrast enhancement of brain metastases can be achieved with a 0.05-mmol/kg dose of Gd-BOPTA at 3.0T compared to imaging at 1.5T using 0.1 mmol/kg Gd-DTPA.
Medical imaging, such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET), plays a vital role for the decision-making in oncologic management.In clinical practice, imaging-derived tumor metrics are routinely applied in oncologic management as an imaging biomarker.For example, the Response Evaluation Criteria in Solid Tumors (RECIST) are commonly used for tumor treatment response evaluation based on the dynamic changes in tumor size.However, the current cross-sectional images are interpreted qualitatively for lesion characterization, treatment response evaluation and prognostic prediction by highly trained radiologists, which has increasingly apparent limitations.Therefore, there is a demanding shift toward more quantitative imaging interpretation.Quantitative imaging, such as diffusion-weighted imaging (DWI), dynamic contrast-enhancement (DCE) and radiomics, refers to objective and systematic measurement derived from digital images, which is different from traditional subjective imaging interpretation.Many researches have demonstrated important usefulness for quantitative imaging in oncology, which outperforms the traditional imaging interpretation approach.In this Special Issue on Quantitative Imaging in Oncologic Management, we organized five articles on the applications of DWI, DCE and radiomics in oncology, involving gastric cancer, esophageal cancer, lung cancer, breast cancer and spinal metastatic tumor.These studies included quantitative imaging for prediction of treatment response, characterization of tumor, differential diagnosis, etc.We hope these studies could provide the oncologists with quantitative imaging approaches in adding the oncologic decision-making.
Some epidemiologic surveillance studies have recorded adverse drug reactions to radiocontrast agents. We aimed to investigate the incidence and management of acute adverse reactions (AARs) to Ultravist-370 and Isovue-370 in patients who underwent contrast-enhanced computed tomography (CT) scanning.Data from 137,473 patients were analyzed. They had undergone enhanced CT scanning with intravenous injection of Ultravist-370 or Isovue-370 during the period of January 1, 2006 to December 31, 2012 in our hospital. We investigated and classified AARs according to the American College of Radiology and the Chinese Society of Radiology (CSR) guidelines for iodinated contrast media. We analyzed risk factors for AARs and compared the AARs induced by Ultravist-370 and Isovue-370.Four hundred and twenty-eight (0.31%) patients experienced AARs, which included 330 (0.24%) patients with mild AARs, 82 (0.06%) patients with moderate AARs, and 16 (0.01%) patients with severe AARs (including 3 cases of cardiac arrest and one case of death). The incidence of AARs was higher with Ultravist-370 than with Isovue-370 (0.38% vs 0.24%, P < 0.001), but only for mild AARs (0.32% vs 0.16%, P < 0.001). Analyses on risk factors indicated that female patients (n = 221, 0.43%, P < 0.001), emergency patients (n = 11, 0.51%, P < 0.001), elderly patients aged 50 to 60 years (n = 135, 0.43%, P < 0.001), and patients who underwent coronary computed tomography angiography (CTA) (n = 55, 0.51%, P < 0.001) had a higher risk of AARs. Cutaneous manifestations (50.52%)-especially rash (59.74%)-were the most frequent mild AARs. Cardiovascular manifestations accounted for most moderate and severe AARs (62.91% and 48.28%, respectively). After proper management, the symptoms and signs of 96.5% of the AARs resolved within 24 hours without sequelae.Ultravist-370 and Isovue-370 are safe for patients undergoing enhanced CT scanning. The incidence of AARs is higher with Ultravist-370 than with Isovue-370, but this difference is limited only to the mild AARs. The incidence of AARs could be affected by multiple factors.
Abstract Background With the advance in digital pathology and artificial intelligence (AI)‐powered approaches, necrosis is proposed as a marker of poor prognosis in colorectal cancer (CRC). However, most previous studies quantified necrosis merely as a tissue type and patch‐level segmentation. Thus, it was worth exploring and validating the prognostic and predictive value of necrosis proportion with a pixel‐level segmentation in large multicenter cohorts. Methods A semantic segmentation model was trained with 12 tissue types labeled by pathologists. Segmentation was performed using the U‐net model with a subsequently derived necrosis tumor ratio (NTR). We proposed the NTR score (NTR‐low or NTR‐high) to evaluate the prognostic and predictive value of necrosis for disease‐free survival (DFS) and overall survival (OS) in the development ( N = 443) and validation cohorts ( N = 333) using 75% as a threshold. Results The 2‐category NTR was an independent prognostic factor and NTR‐low was associated with significant prolonged DFS (unadjusted HR for high vs. low 1.72 [95% CI 1.19–2.49] and 1.98 [1.22–3.23] in the development and validation cohorts). Similar trends were observed for OS. The prognostic value of NTR was maintained in the multivariate analysis for both cohorts. Furthermore, a stratified analysis showed that NTR‐high was a high risk with adjuvant chemotherapy for OS in stage II CRC ( p = 0.047). Conclusion AI‐based pixel‐level quantified NTR has a stable prognostic value in CRC associated with unfavorable survival. Additionally, adjuvant chemotherapy provided survival benefits for patients with a high NTR score in stage II CRC.
Objective: To explore the value of radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiation fat-poor angiomyolipoma (fp-AML) from alpha-fetoprotein-negative hepatocellular carcinoma (n-HCC) in the background of non-cirrhotic liver. Methods: The complete data of 121 patients from Guangdong Provincial People's Hospital, Zhongshan Hospital Affiliated to Fudan University and Sun Yat-sen University Cancer Center with hepatic fp-AML and n-HCC confirmed by pathology from October 2010 to July 2020 were retrospectively analyzed. Among them, 75 were males and 46 were females, aged from 23 to 80 (55±12) years. A total of 93 patients from Zhongshan Hospital Affiliated to Fudan University were divided into the training cohort (n=75) and internal test cohort (n=18) according to entry time, and the patients of other 2 hospitals were divided into external test cohort (n=28). The radiomics features were extracted from the preoperative triple-phase contrast-enhanced images. The feature selection algorithm based on Joint Mutual Information Maximisation (JMIM) was used to extract the optimal feature subset, and support vector machine (SVM) was used to build the radiomics model. The diagnostic performance of radiomics model was evaluated using the receiver operating characteristic (ROC) curve, and was compared with that of two radiologists. Results: In the internal cohort, the area under the curve (AUC) for the differential diagnosis between fp-AML and n-HCC of the radiomics model was 0.819 (with an accuracy of 72.2%), outperforming than radiologist 1 with 10 years of diagnostic experience (AUC=0.542, P=0.029) and radiologist 2 with 2 years of diagnostic experience (AUC=0.375, P=0.004). In the external cohort, the AUC of the radiomics model was 0.772 (with and accuracy of 71.4%), which was comparable to that of radiologist 1 (AUC=0.661, P=0.442) and better than that of radiologist 2 (AUC=0.400, P=0.008). Conclusion: The radiomics model based on dynamic contrast-enhanced MRI is of high accuracy for preoperatively differentiating hepatic fp-AML from n-HCC in the noncirrhotic liver.目的： 探讨基于动态增强MRI影像组学模型术前鉴别非肝硬化背景下乏脂肪型肝血管平滑肌脂肪瘤（fp-AML）和甲胎蛋白阴性肝细胞癌（n-HCC）的价值。 方法： 回顾性分析2010年10月至2020年7月广东省人民医院、中山大学肿瘤防治中心、复旦大学附属中山医院经手术病理证实的121例肝fp-AML和n-HCC患者完整资料，其中男75例，女46例，年龄23~80（55±12）岁。按入组时间顺序，将复旦大学附属中山医院（n=93）的患者划分为训练集（n=75）和内部测试集（n=18），广东省人民医院、中山大学肿瘤防治中心的患者作为外部测试集（n=28）。基于术前三期增强图像提取影像组学特征，使用联合互信息最大化（JMIM）特征选择算法提取最优特征子集，运用支持向量机（SVM）建立影像组学模型，绘制受试者工作特征（ROC）曲线评价模型诊断效能，并与2名放射科医生进行比较。 结果： 影像组学模型鉴别肝fp-AML和n-HCC的曲线下面积（AUC）在内部测试集中为0.819（准确度72.2%），优于诊断经验10年的医生1（AUC=0.542，P=0.029）及2年的医生2（AUC=0.375，P=0.004）；在外部测试集中AUC为0.772（准确度71.4%），与医生1表现相当（AUC=0.661，P=0.442），优于医生2（AUC=0.400，P=0.008）。 结论： 基于动态增强MRI影像组学模型对于术前鉴别非肝硬化背景下肝fp-AML和n-HCC准确度高。.
Abstract Accurate and repeatable measurement of the gross tumour volume(GTV) of subcutaneous xenografts is crucial in the evaluation of anti-tumour therapy. Formula and image-based manual segmentation methods are commonly used for GTV measurement but are hindered by low accuracy and reproducibility. 3D Slicer is open-source software that provides semiautomatic segmentation for GTV measurements. In our study, subcutaneous GTVs from nude mouse xenografts were measured by semiautomatic segmentation with 3D Slicer based on morphological magnetic resonance imaging(mMRI) or diffusion-weighted imaging(DWI)(b = 0,20,800 s/mm 2 ) . These GTVs were then compared with those obtained via the formula and image-based manual segmentation methods with ITK software using the true tumour volume as the standard reference. The effects of tumour size and shape on GTVs measurements were also investigated. Our results showed that, when compared with the true tumour volume, segmentation for DWI(P = 0.060–0.671) resulted in better accuracy than that mMRI(P < 0.001) and the formula method(P < 0.001). Furthermore, semiautomatic segmentation for DWI(intraclass correlation coefficient, ICC = 0.9999) resulted in higher reliability than manual segmentation(ICC = 0.9996–0.9998). Tumour size and shape had no effects on GTV measurement across all methods. Therefore, DWI-based semiautomatic segmentation, which is accurate and reproducible and also provides biological information, is the optimal GTV measurement method in the assessment of anti-tumour treatments.
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