Real-World Utilization, Barriers, and Factors Associated With the Targeted Treatment of Metastatic Colorectal Cancer Patients in China: A Multi-Center, Hospital-Based Survey Study.

Objectives: To explore the utilization, barriers, and factors associated with the targeted treatment of Chinese metastatic colorectal cancer (mCRC) patients. Methods: A total of 1,688 mCRC patients from 19 hospitals in 14 cities were enrolled from March 2020 to March 2021 using stratified, multistage cluster sampling. The use of targeted therapy and any barriers patients experienced were collected. Logistic regression analyses were conducted to identify the factors associated with initiating targeted treatment. Results: About 51.6% of the patients initiated targeted therapy, of whom 44.5%, 20.2%, and 35.2% started first-, second-, and third-line treatment, respectively. The most reported barriers were high medical costs and a lack of belief in the efficacy of targeted therapy. Patients treated in the general hospital, diagnosed at an older age, less educated, and who had a lower family income, no medical insurance, poor health-related quality of life, metastasis outside the liver/lung or systemic metastasis, a shorter duration of mCRC were less likely to initiate targeted therapy. Conclusion: Reduced medical costs and interventional education to improve public awareness could facilitate the use of targeted treatment for mCRC.

Out-of-pocket medical expenditure and associated factors of advanced colorectal cancer in China: a multi-center cross-sectional study.

Background: Colorectal cancer (CRC) causes a substantial disease burden in China. Information on the medical expenditure of CRC patients is critical for decision-makers to allocate medical resources reasonably, however, relevant data is limited in China, especially advanced CRC. The aim of this survey was to quantify the out-of-pocket medical expenditure of advanced CRC and explore associated factors. Methods: A nation-wide, multi-center, cross-sectional survey was conducted from March 2020 to March 2021. Nineteen hospitals in seven geographical regions were selected by multi-stage stratified sampling. For each eligible CRC patient with stage III or IV disease in the selected hospitals, the socio-demographics, clinical information, and range of out-of-pocket medical expenditure data were collected based on patients' self-reporting or medical records. Multivariable logistic analysis was used to explore associated factors of medical expenditure. All statistical analyses were conducted using SAS 9.4. Results: The mean age of the 4,428 advanced CRC patients included was 59.5±11.6 years, 59.6% were male, and 80.1% of patients were in stage III or IV at the time of diagnosis. Besides, 57.2% of patients had an annual household income of less than 50,000 Chinese Yuan (CNY), 40.9% of patients had an out-of-pocket medical expenditure of 50,000-99,999 CNY. As for the affordability of medical expenditure, 33.2% could afford 50,000-99,999 CNY. Multivariate analysis showed that patients who were in the southern [odds ratio (OR): 1.63, 95% confidence interval (CI): 1.31-2.03] and southwestern (OR: 1.55, 95% CI: 1.25-1.93), were in stage III at the time of diagnosis (OR: 1.33, 95% CI: 1.13-1.57), visited three or more hospitals (OR: 1.26, 95% CI: 1.04-1.52), had sought cross-regional health care (OR: 1.60, 95% CI: 1.40-1.83), used genetic testing (OR: 1.26, 95% CI: 1.10-1.45) and targeted drugs (OR: 2.12, 95% CI: 1.79-2.51) had higher out-of-pocket medical expenditure. Conclusions: Patients with advanced CRC had a high out-of-pocket medical expenditure. It is necessary to strengthen the prevention and control of CRC to reduce the disease burden; also, it is critical to deepen the reform of the medical system, increase proportion of medical insurance reimbursement, and remove barriers to cross-regional health care.

Targetable phosphorescent oxygen nanosensors for the assessment of tumor mitochondrial dysfunction by monitoring the respiratory activity.

Cellular respiration is a worthwhile criterion to evaluate mitochondrial dysfunction by measuring the dissolved oxygen. However, most of the existing sensing strategies merely report extracellular (ec-) or intracellular (ic-) O2 rather than intramitochondrial (im-) O2 . Herein we present a method to assess tumor mitochondrial dysfunction with three phosphorescent nanosensors, which respond to ec-, ic-, and im-O2 . Time-resolved luminescence is applied to determine the respective oxygen consumption rates (OCRs) under varying respiratory conditions. Data obtained for the OCRs and on (intra)cellular O2 gradients demonstrate that mitochondria in tumor cells are distinctly less active than those of healthy cells, resulting from restrained glucose utilization of and physical injury to the mitochondria. We believe that such a site-resolved sensing strategy can be applied to numerous other situations, for example to evaluate the adverse effects of drug candidates.

Assessment of performance and reliability of computer-aided detection scheme using content-based image retrieval approach and limited reference database.

Content-based image retrieval approach was used in our computer-aided detection (CAD) schemes for breast cancer detection with mammography. In this study, we assessed CAD performance and reliability using a reference database including 1500 positive (breast mass) regions of interest (ROIs) and 1500 normal ROIs. To test the relationship between CAD performance and the similarity level between the queried ROI and the retrieved ROIs, we applied a set of similarity thresholds to the retrieved similar ROIs selected by the CAD schemes for all queried suspicious regions, and used only the ROIs that were above the threshold for assessing CAD performance at each threshold level. Using the leave-one-out testing method, we computed areas under receiver operating characteristic (ROC) curves (A(Z)) to assess CAD performance. The experimental results showed that as threshold increase, (1) less true positive ROIs can be referenced in the database than normal ROIs and (2) the A(Z) value was monotonically increased from 0.854 ± 0.004 to 0.932 ± 0.016. This study suggests that (1) in order to more accurately detect and diagnose subtle masses, a large and diverse database is required, and (2) assessing the reliability of the decision scores based on the similarity measurement is important in application of the CBIR-based CAD schemes when the limited database is used.

Assessment of performance improvement in content-based medical image retrieval schemes using fractal dimension.

RATIONALE AND OBJECTIVES: The aim of this study was to investigate whether using a fractal dimension as an objective index (quantitative measure) to assess and control the "visual" or "texture" similarity of reference-image regions selected by a content-based image retrieval (CBIR) scheme would (or would not) affect the performance of the scheme in classification between image regions depicting suspicious breast masses. MATERIALS AND METHODS: An image data set depicting 1500 verified mass regions and 1500 false-positive mass regions was used. Fourteen morphologic and intensity distribution features and a fractal dimension were computed. A CBIR scheme using a k-nearest neighbor classifier was applied, and two experiments were conducted. In the first experiment, the CBIR scheme was evaluated using all 15 features. In the second experiment, the fractal dimension was used as a prescreening feature to guide the CBIR scheme to search for the most similar reference images that had similar measures in the fractal dimension. RESULTS: The CBIR scheme achieved classification performance with areas under the receiver-operating characteristic curve of 0.857 (95% confidence interval [CI], 0.844-0.870) using 14 features and 0.866 (95% CI, 0.853-0.879) after adding the fractal dimension (P = .005 for both results). After using the fractal dimension as a prescreening feature, the CBIR scheme achieved an area under the receiver-operating characteristic curve of 0.851 (95% CI, 0.837-0.864), without a significant difference from the previous result using the original 14 features (P = .120). The difference of fractal dimension values between the selected similar reference images was reduced by 56.7%, indicating improvement in image texture similarity. In addition, more than half of references were discarded early, without similarity comparisons, indicating improvement in searching efficiency. CONCLUSIONS: This study demonstrated the feasibility of applying a fractal dimension as an objective (quantitative) and efficient search index to assess and maintain the texture similarity of reference mass regions selected by a CBIR scheme without reducing the scheme's performance in classifying suspicious breast masses.

Improving performance of content-based image retrieval schemes in searching for similar breast mass regions: an assessment.

This study aims to assess three methods commonly used in content-based image retrieval (CBIR) schemes and investigate the approaches to improve scheme performance. A reference database involving 3000 regions of interest (ROIs) was established. Among them, 400 ROIs were randomly selected to form a testing dataset. Three methods, namely mutual information, Pearson's correlation and a multi-feature-based k-nearest neighbor (KNN) algorithm, were applied to search for the 15 'the most similar' reference ROIs to each testing ROI. The clinical relevance and visual similarity of searching results were evaluated using the areas under receiver operating characteristic (ROC) curves (A(Z)) and average mean square difference (MSD) of the mass boundary spiculation level ratings between testing and selected ROIs, respectively. The results showed that the A(Z) values were 0.893 +/- 0.009, 0.606 +/- 0.021 and 0.699 +/- 0.026 for the use of KNN, mutual information and Pearson's correlation, respectively. The A(Z) values increased to 0.724 +/- 0.017 and 0.787 +/- 0.016 for mutual information and Pearson's correlation when using ROIs with the size adaptively adjusted based on actual mass size. The corresponding MSD values were 2.107 +/- 0.718, 2.301 +/- 0.733 and 2.298 +/- 0.743. The study demonstrates that due to the diversity of medical images, CBIR schemes using multiple image features and mass size-based ROIs can achieve significantly improved performance.

[Reduction in cost of stay in intensive care unit with price education program of drugs].

OBJECTIVE: To study if the cost of stay in intensive care unit (ICU) could be reduced by educating intensivists with price of drugs used in ICU. METHODS: A ten-bed ICU of a teaching hospital, with admittance of about 40 patients every month, was involved, and cost of stay was charged by computer system, and it could be calculated and checked by computer on line. Firstly, the knowledge of the prices of 100 drugs usually used in ICU among the 7 intensivists was investigated, then an education program of drug price was launched to train the 7 intensivists,and they were urged those the drugs with lower price instead of higher price treatment results .Finally, the cost of stay in ICU for 2 months before and after the education program was analyzed and their difference was compared. RESULTS: The intensivists understood only the prices of 18% of drugs used in ICU. The cost of drugs was reduced by 8.6% during 2 months after education program (31.8%) compared with that before education program (40.4%), but there was no significant statistical difference (P>0.05). However, the cost of clinical examinations and check-up was lower after education than that before education (P<0.01). The total cost of stay in ICU was 5,688 yuan each day before education program and 5,267 yuan after education program. The total cost daily was reduced by 421 yuan, but with no significant statistical difference (P>0.05). CONCLUSION: The price education program of drugs can reduce the expense of drug and total cost of stay in ICU.

Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography.

OBJECTIVE: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates. MATERIALS AND METHODS: We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard. RESULTS: The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05). CONCLUSION: Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.

Computerized assessment of tissue composition on digitized mammograms.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for the quantitative assessment of breast tissue composition on digitized mammograms. MATERIALS AND METHODS: Three radiologists were asked to review 200 digitized mammograms and independently provide a Breast Imaging Reporting and Data System-like rating for breast tissue composition on a scale of 0 to 4. These values were incorporated into a "consensus" rating that was used as a reference point in the development and evaluation of a computerized method. After tissue segmentation that excluded nontissue areas, a set of quantitative features was computed. A computerized summary index that attempts to reproduce the radiologists' ratings was developed. Correlation coefficients (Pearson r) were used to compare the computerized index with the consensus ratings. RESULTS: Some individual features computed for the relatively dense breast areas showed good correlation (r > 0.8) with the radiologists' subjective ratings. The summary index of tissue composition demonstrated a significant correlation (r = 0.87), as well. CONCLUSION: Computerized methods that show good correlation with radiologists' ratings of breast tissue composition can be developed.