A non-invasive multimodal sono-contrast NIR spectroscopy system for breast cancer diagnosis: Clinical trial

2011 ◽  
Author(s):  
Kaiguo Yan ◽  
Yan Yu ◽  
Lydia Liao
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Cancer Diagnosis ◽  
Nir Spectroscopy ◽  
Breast Cancer Diagnosis ◽  
Non Invasive ◽  
Spectroscopy System

SU-FF-J-26: Multimodal Sono-Contrast NIR Spectroscopy System for Breast Cancer Diagnosis

2009 ◽  
Vol 36 (6Part6) ◽  
pp. 2481-2481
Author(s):  
K Yan ◽  
T Podder ◽  
K Huang ◽  
Y Hu ◽  
J Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Nir Spectroscopy ◽  
Breast Cancer Diagnosis ◽  
Spectroscopy System

scholarly journals Non-Invasive Biomarkers for Early Detection of Breast Cancer

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2767
Author(s):  
Jiawei Li ◽  
Xin Guan ◽  
Zhimin Fan ◽  
Lai-Ming Ching ◽  
Yan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
False Negative ◽  
Breast Cancer Diagnosis ◽  
Diagnostic Biomarkers ◽  
Circulating Micrornas ◽  
Non Invasive ◽  
Common Cancer ◽  
Volatile Organic

Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of “omics” strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Data Flow Chart in a Non-Invasive Breast Cancer Diagnosis System

2007 ◽  
Author(s):  
Mihaela Costin ◽  
Octavian Baltag ◽  
Doina Costandache ◽  
Cipriana Stefanescu
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Invasive Breast Cancer ◽  
Data Flow ◽  
Breast Cancer Diagnosis ◽  
Flow Chart ◽  
Diagnosis System ◽  
Non Invasive

ACUFOCIN: Randomized clinical trial of ACUpuncture plus standard care versus standard care alone FOr Chemotherapy Induced peripheral Neuropathy (CIPN).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 12003-12003 ◽  
Author(s):  
Andrew M. Wardley ◽  
David Ryder ◽  
Vivek Misra ◽  
Peter S Hall ◽  
Peter Mackereth ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Cancer Diagnosis ◽  
Odds Ratio ◽  
Standard Care ◽  
Breast Cancer Diagnosis ◽  
Summary Score ◽  
Mean Difference ◽  
Diagnosis And Treatment ◽  
Grade Ii

12003 Background: CIPN is a dose limiting toxicity, and a major clinical challenge. This study aims to explore the use of acupuncture with standard care (Acu +SC) against SC alone, to reduce symptoms of CIPN. Methods: A phase II, randomised, parallel group design was used to investigate the effectiveness of a 10 week course of acupuncture to manage CIPN. Patients experiencing CIPN ≥ Grade II (CTCAE v4.03), recording a ‘Most Troublesome’ CIPN symptom score of ≥ 3 using the "Measure Yourself Medical Outcome Profile" (MYMOP 2), were randomised (1:1) to either Acu+SC or SC alone. The primary end-point was a ≥ 2 point improvement in MYMOP2 score at week 10 (logistic regression adjusted for stratification factors and baseline MYMOP2 score). The necessary sample size was 100 patients;120 were randomised to allow for attrition (90% power; 10% one-sided alpha), for a hypothesised improvement in success proportions from 30% to 55%. Results: 120 patients were randomised to ACUFOCIN; diagnosis: breast 61 (51%), multiple myeloma 9 (8%), GI 48 (40%), gynaecological 2 (2%). MYMOP2 score for most troubling CIPN symptom at baseline: 3-4 33 (28%), 5-6 87 (73%). CTCAE CIPN at baseline; grade II 103 (86%), grade III 17 (14%). Baseline characteristics were balanced between arms. Primary outcome data were available for 108 participants with 36/54 (67%) successes in the Acu+SC arm compared to 18/55 (33%) in the SC arm. Adjusted success odds ratio was 4.3 (95% CI 1.9-9.6; p < 0.001; Acu+SC vs SC). Additionally, 27/53 (51%) participants achieved a CIPN success (grade ≤ I) in the Acu+SC arm compared to 4/56 (7%) in the SC arm with adjusted odds ratio 13.1 (95% CI 4.1-41.7; p < 0.001; Acu+SC vs SC). Significant reduction in week 10 pain score; mean difference (SC+Acu – SC alone) -1.45 with 95% CI (-2.25, -0.65) after adjustment for week 1 pain, breast cancer diagnosis and treatment complete status. (note pain on a 0-10 scale). Significant increase in the EORTC QLQ-C30 summary score; mean difference (SC+Acu – SC alone) 9.51 with 95% CI (5.01, 14.02) after adjustment for the baseline score, breast cancer diagnosis and treatment complete status. (note summary score on a 0-100 scale). Significant effects seen at week 10 are also present at week 6. The week 6 effect estimates are consistently less than the week 10 effects (but not usually statistically significantly so). Conclusions: In this patient cohort, a 10 week course of acupuncture significantly improved symptoms of CIPN. These results support further investigation within a phase III trial. Clinical trial information: NCT02275403 .

scholarly journals Clinical study of a noninvasive multimodal sono-contrast induced spectroscopy system for breast cancer diagnosis

2012 ◽  
Vol 39 (3) ◽  
pp. 1571-1578 ◽  
Author(s):  
K. Yan ◽  
Y. Yu ◽  
E. Tinney ◽  
R. Baraldi ◽  
L. Liao
Keyword(s):  
Breast Cancer ◽  
Clinical Study ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Spectroscopy System

Breast Cancer Diagnosis Using Relational Discriminant Analysis of Malignancy-Associated Changes

2017 ◽  
pp. 22-42
Author(s):  
Dmitry Klyushin ◽  
Natalia Boroday ◽  
Kateryna Golubeva ◽  
Maryna Prysiazhna ◽  
Maksym Shlykov
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Cancer Diagnostics ◽  
High Rate ◽  
Statistical Machine Learning ◽  
Statistical Depth ◽  
Non Invasive ◽  
Novel Method ◽  
Novel Algorithms

The chapter is devoted to description of a novel method of breast cancer diagnostics based on the analysis of the distribution of the DNA concentration in interphase nuclei of epitheliocytes of buccal epithelium with the aid of novel algorithms of statistical machine learning, namely: novel proximity measure between multivariate samples, novel algorithm of construction of tolerance ellipsoids, novel statistical depth and novel method of multivariate ordering. In contrast to common methods of diagnostics used in oncology, this method is a non-invasive and offers a high rate of accuracy and sensitivity.

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