Evaluation of radiomics as a predictor of tumor hypoxia and response to anti-PD-1 mab treatment (IO) in recurrent/metastatic HNSCC patients (R/M).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6045-6045
Author(s):  
Dan Paul Zandberg ◽  
Serafettin Zenkin ◽  
Murat AK ◽  
Priyadarshini Mamindla ◽  
Vishal Peddagangireddy ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Cross Validation ◽  
Tumor Hypoxia ◽  
Texture Features ◽  
Classification Model ◽  
P Value ◽  
Patient Cohort ◽  
Volume Of Interest ◽  
Feature Selection Approach ◽  
Sensitivity Specificity

6045 Background: There is a great need for non-invasive predictors of the tumor microenvironment and the efficacy of anti-PD-1 mAb treatment (IO) in R/M HNSCC patients. We previously showed that lower tumor hypoxia was associated with increased efficacy with IO ( Journal of Clinical Oncol. 38, no. 15_suppl (May 20, 2020) 6546) and now we evaluate the predictive value of radiomics in this same patient cohort. Methods: We studied radiomic signatures in a cohort of 36 patients with R/M HNSCC treated with IO. Treatment response was evaluated using RECIST 1.1. Patients were categorized as: Responders (R) ie CR, PR, SD and non-Responders (NR) i.e PD. As per our previous analysis (ref above) hypoxia was evaluated on archival FFPE samples via immunofluorescent imaging and defined by the ratio of percent area (% CAIX) / the mean intensity (Int) of carbonic anhydrase IX in tumor (%CAIX/Int). ImageJ software was used to determine %CAIX and Int. Feature extraction was performed on the pre-immunotherapy baseline CT scans. The lesions were segmented using 3D slicer v4.10.2 to create a volume of interest (VOI) for radiomic texture analysis (TA). A total of 400 features (10 histogram-based and 390 second-order texture features) were calculated from each extracted volume of interest (VOI). Radiomic features were obtained using a feature selection approach based on Least Absolute Shrinkage and Selection Operator (LASSO). Selected features were used to build a classification model, using XGboost, for prediction of tumor response to immunotherapy. Cross-validation was performed using the Leave One Out Cross Validation (LOOCV) approach for the XGBoost method to evaluate the robustness of the estimates and calculated accuracy, sensitivity, specificity and p-value. Results: Our patient cohort had a median age of 59, 69% male, 58% smokers. 61% received IO for platinum failure, 39% frontline. Primary site included 39% OC, 22% OPC (38% HPV positive), 17% Larynx, 5% hypopharynx, and 17% other. Radiomics applied to the primary HNSCC tumor highly predicted tumor hypoxia status with a sensitivity, specificity, and accuracy of 78%, 83%, and 81%, respectively, p = 0.0001. To predict response, we applied radiomics to both the primary HNSCC tumor and pathological lymph nodes; radiomics was also able to predict whether a patient would be a responder (N = 8) versus a non-responder (N = 28) to IO based on the pre-immunotherapy baseline CT scan. The sensitivity, specificity, and accuracy were 93%, 88%, and 92%, respectively, p = 0.02. Conclusions: Even in a small cohort, radiomics could predict response to IO and tumor hypoxia in R/M HNSCC patients. To our knowledge this is the first evaluation of this kind in this patient population. Further evaluation of radiomics as a predictor of efficacy with IO and the tumor microenvironment is warranted.

scholarly journals Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 277
Author(s):  
Zuzanna Anna Magnuska ◽  
Benjamin Theek ◽  
Milita Darguzyte ◽  
Moritz Palmowski ◽  
Elmar Stickeler ◽  
...  
Keyword(s):  
Breast Lesion ◽  
Characteristic Curve ◽  
Cancer Diagnostics ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
P Value ◽  
Future Design ◽  
Sensitivity Specificity ◽  
Derivatives Of

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

scholarly journals Ensemble Feature Selection for Breast Cancer Classification using Microarray Data

2020 ◽  
Vol 23 (65) ◽  
pp. 100-114
Author(s):  
Supoj Hengpraprohm ◽  
Suwimol Jungjit
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cross Validation ◽  
Microarray Dataset ◽  
Classification Model ◽  
Cancer Data ◽  
Evaluation Functions ◽  
Selection For ◽  
Feature Selection Approach ◽  
Fold Cross Validation

For breast cancer data classification, we propose an ensemble filter feature selection approach named ‘EnSNR’. Entropy and SNR evaluation functions are used to find the features (genes) for the EnSNR subset. A Genetic Algorithm (GA) generates the classification ‘model’. The efficiency of the ‘model’ is validated using 10-Fold Cross-Validation re-sampling. The Microarray dataset used in our experiments contains 50,739 genes for each of 32 patients. When our proposed ‘EnSNR’ subset of features is used; as well as giving an enhanced degree of prediction accuracy and reducing the number of irrelevant features (genes), there is also a small saving of computer processing time.

scholarly journals A Quantitative Lung Computed Tomography Image Feature for Multi-Center Severity Assessment of COVID-19

2020 ◽  
Author(s):  
Biswajoy Ghosh ◽  
Nikhil Kumar ◽  
Nitisha Singh ◽  
Anup K. Sadhu ◽  
Nirmalya Ghosh ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Texture Features ◽  
High Sensitivity ◽  
Image Feature ◽  
P Value ◽  
Severity Assessment ◽  
Machine Learning Classification ◽  
Therapeutic Decisions ◽  
Computed Tomography Image ◽  
Sensitivity Specificity

AbstractThe COVID-19 pandemic has affected millions and congested healthcare systems globally. Hence an objective severity assessment is crucial in making therapeutic decisions judiciously. Computed Tomography (CT)-scans can provide demarcating features to identify severity of pneumonia —commonly associated with COVID-19—in the affected lungs. Here, a quantitative severity assessing chest CT image feature is demonstrated for COVID-19 patients. We incorporated 509 CT images from 101 diagnosed and expert-annotated cases (age 20-90, 60% males) in the study collected from a multi-center Italian database1 sourced from 41 radio-diagnostic centers. Lesions in the form of opacifications, crazy-paving patterns, and consolidations were segmented. The severity determining feature —Lnorm was quantified and established to be statistically distinct for the three —mild, moderate, and severe classes (p-value<0.0001). The thresholds of Lnorm for a 3-class classification were determined based on the optimum sensitivity/specificity combination from Receiver Operating Characteristic (ROC) analyses. The feature Lnorm classified the cases in the three severity categories with 86.88% accuracy. ‘Substantial’ to ‘almost-perfect’ intra-rater and inter-rater agreements were achieved involving expert (manual segmentation) and non-expert (graph-cut and deep-learning based segmentation) labels (κ-score 0.79-0.97). We trained several machine learning classification models and showed Lnorm alone has a superior diagnostic accuracy over standard image intensity and texture features. Classification accuracy was further increased when Lnorm was used for 2-class classification i.e. to delineate the severe cases from non-severe ones with a high sensitivity (97.7%), and specificity (97.49%). Therefore, key highlights of the COVID-19 severity assessment feature are high accuracy, low dependency on expert availability, and wide utility across different CT-imaging centers.

scholarly journals RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound

2021 ◽  
Vol 11 (24) ◽  
pp. 12138
Author(s):  
Shahriar Mahmud Kabir ◽  
Mohammed I. H. Bhuiyan ◽  
Md Sayed Tanveer ◽  
ASM Shihavuddin
Keyword(s):  
Breast Tumor ◽  
Texture Features ◽  
Classification Performance ◽  
Tumor Classification ◽  
P Value ◽  
Ultrasound Images ◽  
Custom Made ◽  
Feature Based ◽  
Sensitivity Specificity ◽  
Breast Tumor Classification

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.

scholarly journals A simple model for glioma grading based on texture analysis applied to conventional brain MRI

2020 ◽  
Author(s):  
José Gerardo Suárez-García ◽  
Javier Miguel Hernández-López ◽  
Eduardo Moreno-Barbosa ◽  
Benito de Celis-Alonso
Keyword(s):  
Texture Analysis ◽  
Low Cost ◽  
Brain Mri ◽  
Texture Features ◽  
Classification Model ◽  
Low Grade ◽  
Mri Contrast ◽  
Glioma Grading ◽  
Sensitivity Specificity

AbstractAccuracy of glioma grading is fundamental for the diagnosis, treatment planning and prognosis of patients. The purpose of this work was to develop a low cost and easy to implement classification model which distinguishes low grade gliomas (LGGs) from high grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. Different combinations between MRI contrasts (T1Gd and T2) and one segmented glioma region (necrotic and non-enhancing tumor core (NCR/NET)) were studied. Texture features obtained from the Gray Level Size Zone Matrix (GLSZM) were calculated. An under-samplig method was proposed to divide the data into different training subsets and subsequently extract complementary information for the creation of distinct classification models. The sensitivity, specificity and accuracy of the models were calculated. The best model was explicitly reported. The best model included only three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 88.24% and 91.18% respectively. According to the features of the model, when the NCR/NET region was studied, HGGs had a more heterogeneous texture than LGGs in the T1Gd images and LGGs had a more heterogeneous texture than HGGs in the T2 images. These novel results partially contrast with results from literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that heterogeneity of gliomas depended on the studied MRI contrast. The model presented stands out as a simple, low cost, easy to implement, reproducible and highly accurate glioma classifier. What is more important, it should be accessible to populations with reduced economic and scientific resources.

scholarly journals Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers

2021 ◽  
Vol 9 (4) ◽  
pp. e001752
Author(s):  
Rivka R Colen ◽  
Christian Rolfo ◽  
Murat Ak ◽  
Mira Ayoub ◽  
Sara Ahmed ◽  
...  
Keyword(s):  
Partial Response ◽  
Stable Disease ◽  
Progressive Disease ◽  
Tumor Response ◽  
Complete Response ◽  
Classification Model ◽  
P Value ◽  
Rare Cancer ◽  
Rare Cancers ◽  
Contrast Enhanced Ct

BackgroundWe present a radiomics-based model for predicting response to pembrolizumab in patients with advanced rare cancers.MethodsThe study included 57 patients with advanced rare cancers who were enrolled in our phase II clinical trial of pembrolizumab. Tumor response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and immune-related RECIST (irRECIST). Patients were categorized as 20 “controlled disease” (stable disease, partial response, or complete response) or 37 progressive disease). We used 3D-slicer to segment target lesions on standard-of-care, pretreatment contrast enhanced CT scans. We extracted 610 features (10 histogram-based features and 600 second-order texture features) from each volume of interest. Least absolute shrinkage and selection operator logistic regression was used to detect the most discriminatory features. Selected features were used to create a classification model, using XGBoost, for the prediction of tumor response to pembrolizumab. Leave-one-out cross-validation was performed to assess model performance.FindingsThe 10 most relevant radiomics features were selected; XGBoost-based classification successfully differentiated between controlled disease (complete response, partial response, stable disease) and progressive disease with high accuracy, sensitivity, and specificity in patients assessed by RECIST (94.7%, 97.3%, and 90%, respectively; p<0.001) and in patients assessed by irRECIST (94.7%, 93.9%, and 95.8%, respectively; p<0.001). Additionally, the common features of the RECIST and irRECIST groups also highly predicted pembrolizumab response with accuracy, sensitivity, specificity, and p value of 94.7%, 97%, 90%, p<0.001% and 96%, 96%, 95%, p<0.001, respectively.ConclusionOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.InterpretationOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.

scholarly journals Komparasi Metode Klasifikasi untuk Deteksi Ekspresi Wajah Dengan Fitur Facial Landmark

2021 ◽  
Vol 8 (5) ◽  
pp. 949
Author(s):  
Fitra A. Bachtiar ◽  
Muhammad Wafi
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Cross Validation ◽  
Classification Model ◽  
Expression Recognition ◽  
Classification Methods ◽  
Human Machine Interaction ◽  
Facial Landmark ◽  
Total Data ◽  
Machine Interaction

<p><em>Human machine interaction</em>, khususnya pada <em>facial</em> <em>behavior</em> mulai banyak diperhatikan untuk dapat digunakan sebagai salah satu cara untuk personalisasi pengguna. Kombinasi ekstraksi fitur dengan metode klasifikasi dapat digunakan agar sebuah mesin dapat mengenali ekspresi wajah. Akan tetapi belum diketahui basis metode klasifikasi apa yang tepat untuk digunakan. Penelitian ini membandingkan tiga metode klasifikasi untuk melakukan klasifikasi ekspresi wajah. Dataset ekspresi wajah yang digunakan pada penelitian ini adalah JAFFE dataset dengan total 213 citra wajah yang menunjukkan 7 (tujuh) ekspresi wajah. Ekspresi wajah pada dataset tersebut yaitu <em>anger</em>, <em>disgust</em>, <em>fear</em>, <em>happy</em>, <em>neutral</em>, <em>sadness</em>, dan <em>surprised</em>. Facial Landmark digunakan sebagai ekstraksi fitur wajah. Model klasifikasi yang digunakan pada penelitian ini adalah ELM, SVM, dan <em>k</em>-NN. Masing masing model klasifikasi akan dicari nilai parameter terbaik dengan menggunakan 80% dari total data. 5- <em>fold</em> <em>cross-validation</em> digunakan untuk mencari parameter terbaik. Pengujian model dilakukan dengan 20% data dengan metode evaluasi akurasi, F1 Score, dan waktu komputasi. Nilai parameter terbaik pada ELM adalah menggunakan 40 hidden neuron, SVM dengan nilai  = 10<sup>5</sup> dan 200 iterasi, sedangkan untuk <em>k</em>-NN menggunakan 3 <em>k</em> tetangga. Hasil uji menggunakan parameter tersebut menunjukkan ELM merupakan algoritme terbaik diantara ketiga model klasifikasi tersebut. Akurasi dan F1 Score untuk klasifikasi ekspresi wajah untuk ELM mendapatkan nilai akurasi sebesar 0.76 dan F1 Score 0.76, sedangkan untuk waktu komputasi membutuhkan waktu 6.97´10<sup>-3</sup> detik.   </p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract">H<em>uman-machine interaction, especially facial behavior is considered to be use in user personalization. Feature extraction and classification model combinations can be used for a machine to understand the human facial expression. However, which classification base method should be used is not yet known. This study compares three classification methods for facial expression recognition. JAFFE dataset is used in this study with a total of 213 facial images which shows seven facial expressions. The seven facial expressions are anger, disgust, fear, happy, neutral, sadness, dan surprised. Facial Landmark is used as a facial component features. The classification model used in this study is ELM, SVM, and k-NN. The hyperparameter of each model is searched using 80% of the total data. 5-fold cross-validation is used to find the hyperparameter. The testing is done using 20% of the data and evaluated using accuracy, F1 Score, and computation time. The hyperparameter for ELM is 40 hidden neurons, SVM with  = 105 and 200 iteration, while k-NN used 3 k neighbors. The experiment results show that ELM outperforms other classification methods. The accuracy and F1 Score achieved by ELM is 0.76 and 0.76, respectively. Meanwhile, time computation takes 6.97 10<sup>-3</sup> seconds.      </em></p>

scholarly journals 644 Tumor-mediated suppressive signaling and hypoxia supports altered chromatin landscapes that limit the transcriptional and functional potential of terminal T cell exhaustion

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A673-A673
Author(s):  
Rhodes Ford ◽  
Natalie Rittenhouse ◽  
Nicole Scharping ◽  
Paolo Vignali ◽  
Greg Delgoffe ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Tumor Microenvironment ◽  
Histone Modifications ◽  
Cd8 T Cells ◽  
Tumor Hypoxia ◽  
T Cell Subsets ◽  
T Cell Exhaustion ◽  
Cell Exhaustion

BackgroundCD8+ T cells are a fundamental component of the anti-tumor response; however, tumor-infiltrating CD8+ T cells (TIL) are rendered dysfunctional by the tumor microenvironment. CD8+ TIL display an exhausted phenotype with decreased cytokine expression and increased expression of co-inhibitory receptors (IRs), such as PD-1 and Tim-3. The acquisition of IRs mark the progression of dysfunctional TIL from progenitors (PD-1Low) to terminally exhausted (PD-1+Tim-3+). How the chromatin landscape changes during this progression has not been described.MethodsUsing a low-input ChIP-based assay called Cleavage Under Targets and Release Using Nuclease (CUT&RUN), we have profiled the histone modifications at the chromatin of tumor-infiltrating CD8+ T cell subsets to better understand the relationship between the epigenome and the transcriptome as TIL progress towards terminal exhaustion.ResultsWe have identified two epigenetic characteristics unique to terminally exhausted cells. First, we have identified a unique set of genes, characterized by active histone modifications that do not have correlated gene expression. These regions are enriched for AP-1 transcription factor motifs, yet most AP-1 family factors are actively downregulated in terminally exhausted cells, suggesting signals that promote downregulation of AP-1 expression negatively impacts gene expression. We have shown that inducing expression of AP-1 factors with a 41BB agonist correlates with increased expression of these anticorrelated genes. We have also found a substantial increase in the number of genes that exhibit bivalent chromatin marks, defined by the presence of both active (H3K4me3) and repressive (H3K27me3) chromatin modifications that inhibit gene expression. These bivalent genes in terminally exhausted T cells are not associated with plasticity and represent aberrant hypermethylation in response to tumor hypoxia, which is necessary and sufficient to promote downregulation of bivalent genes.ConclusionsOur study defines for the first time the roles of costimulation and the tumor microenvironment in driving epigenetic features of terminally exhausted tumor-infiltrating T cells. These results suggest that terminally exhausted T cells have genes that are primed for expression, given the right signals and are the basis for future work that will elucidate that factors that drive progression towards terminal T cell exhaustion at the epigenetic level and identify novel therapeutic targets to restore effector function of tumor T cells and mediate tumor clearance.

scholarly journals Performance of the BD FACSPresto near to patient Analyzer in comparison with representative conventional CD4 instruments in Cameroon.

2020 ◽  
Author(s):  
Bertrand Sagnia ◽  
Fabrice MBAKOP Ghomsi ◽  
Ana Gutierrez ◽  
Samuel SOSSO ◽  
Rachel KAMGAING ◽  
...  
Keyword(s):  
T Lymphocytes ◽  
T Lymphocyte ◽  
Statistical Significance ◽  
P Value ◽  
Resource Constrained ◽  
Inform Consent ◽  
Remote Areas ◽  
Significant Difference ◽  
Sensitivity Specificity ◽  
Cd4 Testing

Abstract Background In the context of scaling the viral load in resource limited settings, following HIV infected patient’s adults and children with CD4+ T-lymphocyte count still very important in settings where the decentralization of treatment still has some challenges. Effective HIV monitoring in these resource-constrained settings needs affordable and reliable CD4+ T lymphocytes enumeration methods. We investigated the validity of a BD FACSPresto POC which is a dedicated system for enumeration that uses immunofluorescent technologies. In this study, we have assessed the sensitivity, specificity and correlation between most representative flow cytometry instruments present in Cameroon with more than 5000 CD4 T cells tests per year including FACSCalibur, FACSCount, and PIMA POC from Becton dinkinson and ALERE respectively. Methods 268 patients aged from 1 to 72 years old were enrolled and included in the study after inform consent. The BD FACSPresto POC CD4+ T cell technology was placed at CIRCB and operated by technician staff. HIV infected patients were from Chantal BIYA international reference Center (CIRCB), Centre de Sante Catholique de NKOLODOM, Centre de Sante Catholique de BIKOP and CASS de Nkolndongo – Yaounde We compared the accuracy of the BD FACSPresto and three existing reference technologies with more than 5000 tests per year like FACSCalibur, FACSCount and PIMA according to the number of CD4 test done per year and their repartition in the country. Bland – Altman method and correlation analysis were used to estimate mean bias and 95% limits of agreement and to compare the methods, including analysis by subgroup of participant gestational age. In addition sensitivity and specificity were determined. Statistical significance was set at p-value < 0.05 Results The BD FACSPresto POC system has excellent precision, accuracy and linearity for CD4+ T lymphocytes enumeration. Good correlations were obtained between the BD FACSPresto poc system and other single platform methods. Bland–Altman plots showed interchangeability between two machines mean bias BD-FACSPresto vs PIMA= -126,522(-161,221 to -91,822) BD-FACSPresto vs FACSCount= -38,708 (-58,935 to -18,482) and FACSPresto vs FACSCALIBUR= 0,791(-11,908 to 13,491). Mean difference with Absolute CD4+ T-lymphocyte values obtained from the BD FACSPresto system correlated well with PIMA, FACSCount, and FACSCalibur method with R 2 equal to 0.88, 0.92 and 0.968 respectively with P < 0.001 for all. The mean comparison between values obtained from BD FACSPresto with PIMA, FACSCount, and FACSCalibur using paired T test give P=0.17, P=0.5 and P=0.6 respectively meaning that there is no significant differences between values obtained with BD FACSPresto and PIMA, FACSCount or FACSCalibur CD4 enumeration machines. Further analysis revealed close agreement between all the three instruments with no significant difference between the forth methods (P=0.91) Conclusion This BD-FACSPresto POC system is a simple, robust and reliable system for enumeration of absolute and percentage of CD4+ T-lymphocytes especially suitable for remote areas with limited resources. Having one BD-FACSPresto POC system easy to use, should reduce the cost and thus increase and improved access to CD4 testing for HIV infected patients in resource-constrained countries. BD-FACSPresto POC CD4 will enable reduction in patient time and improve the overall quality of ART service count and may improve test access in remote areas. This technology can allow for greater decentralization and wider access to CD4 testing and ART

Sign in / Sign up

Export Citation Format

Share Document