Measurement of breast-tissue x-ray attenuation by spectral imaging: fresh and fixed normal and malignant tissue

Diagnosis of Canine Tumours and the Value of Combined Detection of VEGF, P53, SF and NLRP3 for the Early Diagnosis of Canine Mammary Carcinoma

The average life of a dog is generally maintained at ten to fifteen years, and tumours are the predominant reason that leads to the death of dogs, especially canine mammary carcinoma. Therefore, early diagnosis of tumours is very important. In this study, tumor size, morphology, and texture could be seen through general clinical examination, tumor metastasis could be seen through imaging examination, inflammatory reactions could be seen through hematological examination, and abnormal cell morphology could be seen through cytological and histopathological examination. In the 269 malignant cases and 179 benign cases, we randomly selected 30 cases each, and an additional 30 healthy dogs were selected for the experiment (healthy dogs: dogs in good physical condition without any tumor or other diseases). We used RT-qPCR and ELISA to determine the relative expression of vascular endothelial growth factor (VEGF), tumor protein P53 (P53), serum ferritin (SF), and NOD-like receptor protein 3 (NLRP3) in 30 healthy dogs, 30 dogs with benign mammary tumours, and 30 dogs with malignant mammary tumours. In the results, the same expression trend was obtained both in serum and tissues, and the expression of the four markers was the highest in malignant mammary tumours, with highly significant differences compared with the benign and healthy/paracancerous groups. By plotting the ROC curves, it was found that the results of combined tests were better than a single test and the combination of the four markers was the best for the early diagnosis. In conclusion, this can assist the clinical early diagnosis to a certain extent, and also provides some references and assistance for the development of tumor detection kits in clinical practice.

A blurring correction method suitable to analyze quantitative x-ray images derived from energy-resolving photon counting detector

Objective.The purpose of this study is to propose a novel blurring correction method that enables accurate quantitative analysis of the object edge when using energy-resolving photon counting detectors (ERPCDs). Although the ERPCDs have the ability to generate various quantitative analysis techniques, such as the derivations of effective atomic number (Zeff) and bone mineral density values, at the object edge in these quantitative images, accurate quantitative information cannot be obtained. This is because image blurring prevents the gathering of accurate primary x-ray attenuation information.Approach.We developed the following procedure for blurring correction. A 5 × 5 pixels masking region was set as the processing area, and the pixels affected by blurring were extracted from the analysis of pixel value distribution. The blurred pixel values were then corrected to the proper values estimated by analyzing minimum and/or maximum values in the set mask area. The suitability of our correction method was verified by a simulation study and an experiment using a prototype ERPCD.Main results. WhenZeffimage of aluminum objects (Zeff= 13) were analyzed without applying our correction method, regardless of raw data or correction data applying a conventional edge enhancement method, the properZeffvalues could not be derived for the object edge. In contrast, when applying our correction method, 82% of pixels affected by blurring were corrected and the properZeffvalues were calculated for those pixels. As a result of investigating the applicability limits of our method through simulation, it was proven that it works effectively for objects with 4 × 4 pixels or more.Significance. Our method is effective in correcting image blurring when the quantitative image is calculated based on multiple images. It will become an in-demand technology for putting a quantitative diagnosis into actual medical examinations.

Additive value of exposure parameters for breast cancer diagnosis in digital mammography

OBJECTIVES

To investigate the effect of different breast lesions on exposure parameters in digital mammography and to determine whether the exposure parameters can additively improve diagnostic efficiency.

METHODS

Craniocaudal view and mediolateral view full-field digital mammography images from 982 women with unilateral lesions (341 with malignant lesions, 189 with benign lesions, and 452 healthy women) obtained at Nanfang Hospital were reviewed. Differences in exposure parameters (tube voltage and load, breast thickness (BT), and average glandular dose (AGD)) between breasts were calculated. The relationships between parameter differences and lesion size were explored. A logistic regression model was used based on the AGD and BT differences, and the area under the receiver operating characteristic curve (AUC) was used to assess the performance of these parameters in differentiating malignant from benign and healthy subjects. Independently, data from 129 women (82 with malignant and 47 with benign lesions) treated at Sun Yat-sen Memorial Hospital were collected to validate the model.

RESULTS

Differences in tube voltage and load, BT, and AGD between breasts were significantly greater in the malignant subjects than benign (p < 0.05) and healthy subjects (p < 0.05). The AUCs for the comparisons of malignant vs. healthy subjects, malignant vs. benign subjects, and benign vs. healthy subjects were 0.77 ± 0.02, 0.72 ± 0.02, and 0.57 ± 0.02, respectively. The model combining the exposure parameters with the BI-RADS category resulted in a higher AUC (0.910 ± 0.03) compared with physician diagnosis alone (0.820 ± 0.04) for differentiating between malignant and benign lesions.

CONCLUSIONS

Exposure parameters additively improved diagnostic accuracy for breast cancer and yielded more reliable results.

KEY POINTS

• Differences in kVp, mAs, BT, and AGD between breasts were significantly greater in the malignant subjects than benign and healthy subjects. • The model combining exposure parameters with the BI-RADS category resulted in a higher AUC compared with the physician's diagnosis for differentiating between malignant and benign lesions. • Exposure parameters additively improved diagnostic accuracy for breast cancer.

Field cancerization in the understanding of parenchymal analysis of mammograms for breast cancer risk assessment

In recent years, mammographic image analysis has shown great potential for breast cancer risk assessment. The aim of risk assessment is to predict how likely a woman is to develop breast cancer in the future. Several studies suggest that computerized parenchymal analysis of mammograms can be utilized as an independent imaging biomarker of breast cancer. Parenchymal analysis consists of the quantitative assessment of visual texture patterns in mammograms to infer the level of risk. In spite of substantial evidence of the association between parenchymal patterns and breast cancer risk, its biological foundations remain poorly understood. In this work, we draw a hypothesis that links the field cancerization (FC) with breast cancer risk assessment based on the parenchymal analysis. In the literature, the FC is interpreted as a biochemical anomaly amplification in otherwise healthy cells due to the effect of pre-cancerous transformed cells in surrounding regions. Our hypothesis is that these biochemical anomaly amplifications change the cellular micro-environment which, in turn, alter tissue responses to X-ray radiation. As a result, it is reasonable to think that these changes influence the interaction of X-rays with parenchymal - the functional - breast tissue thus enabling cancer prediction by analyzing X-ray images of the breast. We believe that our hypothesis provides an actionable explanation as to how computerized parenchymal analysis of apparently normal mammograms can be successfully utilized for the stratification of breast cancer risk.

Quantitative characterization of breast tissues with dedicated CT imaging

A quantitative characterization of the soft tissues composing the human breast is achieved by means of a monochromatic CT phase-contrast imaging system, through accurate measurements of their attenuation coefficients within the energy range of interest for breast CT clinical examinations. Quantitative measurements of linear attenuation coefficients are performed on tomographic reconstructions of surgical samples, using monochromatic x-ray beams from a synchrotron source and a free space propagation setup. An online calibration is performed on the obtained reconstructions, in order to reassess the validity of the standard calibration procedure of the CT scanner. Three types of healthy tissues (adipose, glandular, and skin) and malignant tumors, when present, are considered from each sample. The measured attenuation coefficients are in very good agreement with the outcomes of similar studies available in the literature, although they span an energy range that was mostly neglected in the previous studies. No globally significant differences are observed between healthy and malignant dense tissues, although the number of considered samples does not appear sufficient to address the issue of a quantitative differentiation of tumors. The study assesses the viability of the proposed methodology for the measurement of linear attenuation coefficients, and provides a denser sampling of attenuation data in the energy range useful to breast CT.