Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer death among women in the United States. According the Breast Cancer Facts and Figures reported by American Cancer Society, in 2017, approximately 252,710 new cases of invasive breast cancer and 60,290 new cases of in situ breast cancer were expected to be diagnosed among US women, and 40,610 US women were expected to die from breast cancer. Early detection in order to improve breast cancer outcomes and survival remains the cornerstone of breast cancer control. As many studies show early detection of breast cancer plays an important role in reducing the associated morbidity and mortality rates and gives women a better chance of full recovery. The significant decrease in breast cancer mortality rate, which amounts to 37% since 1990, is a major medical success and is due in large part to the earlier detection of breast cancer and the follow-up to the women with high risk of breast cancer through mammography. In addition, early detection will save a significant amount of medical resources for breast cancer treatment.
Currently, the most effective method for breast cancer early detection is screening mammography. However, mammography has relatively low sensitivity to detect small breast cancers (under several millimeters). Specificity and the positive predictive value of mammography remain limited, owing to structure and tissue overlap. Limited sensitivity and specificity in breast cancer detection of mammography are due to its poor contrast detectability, which is common for all types of projection imaging techniques (projection imaging can only have up to 10% contrast detectability), and mammography initially detects only 65-70% of breast cancers. The sensitivity of mammography is further reduced to as low as 30% in the dense breast.
Traditional x-ray technology, no matter how good its quality, does not contain all the necessary information for many radiologists to determine whether the abnormality detected is a cancer, requiring surgery, or a benign lesion, which can be followed by additional imaging procedures. Therefore, in order to avoid a misdiagnosis, the radiologist may consider a follow up examination or recommend a biopsy to be accurate. However, up to 80% of biopsies turn out to be negative or unnecessary. Unnecessary biopsy brings huge burden to the medical cost and patient’s emotional condition.
Computed Tomography Laser Mammography (CTLM®) was developed to enhance conventional breast screening methods, specifically with the dense breast screening population. Jin Qi, MD, Visiting Research Associate at Moffitt Cancer Center & Research Institute was the only breast radiologist to provide clinical advice and image evaluation, as well as lead clinical studies to evaluate and improve the technology and product during CTLM’s development.
According the datasheet (http://www.imds.com), CTLM uses laser technology to scan the breast. The laser is tuned to a specific near-infrared wavelength of 808 nanometers (nm) to image the blood distribution within the breast by showing an attenuation absorption difference between hemoglobin and water or fat molecules. This principle enables the CTLM to produce 3D images of hemoglobin distribution in the breast while tissues rich in fat and water appear transparent.
On CTLM, angiogenesis will appear much larger, and therefore easier to see, than the original lesion on the mammogram. In fact, a tumor which is only 3.0 mm in size on a mammogram will usually have an area of angiogenesis which is 4 to 6 cm in size on CTLM image.
CTLM is a breakthrough in breast imaging technology. For the first time, people will be able to understand the tumor angiogenesis without X-ray and any other expensive and high risk method, such as MRI.
From 2007 to 2009, Jin Qi M.D. from Tianjin Cancer Hospital, was the principle investigator of the world’s first study on CTLM, “CTLM as An Adjunct to Mammography in Dense Breast”. This study acquired the mammography, CTLM, and pathology data of 155 women. By comparing the diagnostic results from mammography and CTLM with pathology report, Dr. Qi found that with CTLM positive lesions were observed more significantly in malignant than benign lesions. The sensitivity of mammography vs. mammography + CTLM was 34.4% vs. 81.57%, respectively among extremely dense breasts and 68.29% vs. 95.34%, respectively among heterogeneously dense breasts. Based on these results, Dr. Qi reached the conclusion that CTLM could distinguish benign lesions from malignant lesions and is not affected by breast density.
“This is the first time in the world that a modality solved the most difficult issue of breast imaging, density. I am so proud to have been involved in this work and be able to present these groundbreaking results to the world,” Dr. Qi stated.
According to the study, advantages of CTLM include:
- CTLM clearly indicates the tumor location by highlighting the blood flow. Combined with another modality, CTLM provides significant information for cancer detection, especially in dense breasts where no tumor can be detected with current technology.
- CTLM has no radiation. It is a minimum risk medical device and is completely safe for women to use for breast cancer screening and diagnosis.
- CTLM is very comfortable. Unlike mammography which requires more than 20 pounds of compression force applied to the breast, CTLM allows women to comfortably lie prone with the breast in a natural pendent position for the scan. All of the study subjects reported a much better experience with CTLM than with mammography.
Jin Qi earned her M.D. in 2003 from Tianjin Medical University, where she was awarded the scholarship of Wangkechang as outstanding student. She was then certified by Ministry of Health of the People’s Republic of China as a Diagnostic Radiologist in 2004. Since then, Dr. Qi has spent her time in clinical practice and research at Tianjin Cancer Hospital, the top ranked cancer hospital in China.
Due to the vast development in computer technology, Dr. Qi chose to obtain her PhD in computer science. In 2013, she joined the PhD program at the School of Computer Science and Technology of Tianjin University. She chose this program hoping to combine her clinical knowledge and expertise with computer technology to introduce significant breakthroughs into the clinical practice.
Dr. Qi focuses on imaging diagnosis and interventional treatment of the lungs, breast, digestive system and prostate. As a diagnostic radiologist, she was responsible for reading roughly 100 medical images every day and more than 40,000 cases every year. Due to her outstanding performance, Dr. Qi became the youngest attending physician in the department of radiology in Tianjin Cancer Hospital. She was also awarded the First Prize of Young Researchers, Science-Technology Achievements Award of the city of Tianjin, Outstanding Physicians, and Outstanding Leading Physicians of Tianjin Cancer Hospital.
Dr. Qi is now currently visiting H. Lee Moffitt Cancer Center & Research Institute as a research associate. She joined Dr. Robert J. Gillie’s team at Moffitt Cancer Center, one of the top teams in the world researching Radiomics and related clinical applications. The delicate structure of Radiomics requires pioneers in this field to be extremely experienced doctors and scientists who fully understand both computer science and clinical radiology. As one of the pioneers of Radiomics, Dr. Qi has been focusing on feature extraction and clinical interpretation of tumor images from various medical imaging modalities including CT and MR.
Dr. Qi’s outstanding research on breast cancer and CTLM makes her the world’s first physician who fully understands the clinical value of this new technology. Now she is not only focused on the clinical application of the new technology but also dedicated in the research and development of the computer analysis of the CTLM imaging currently in laboratory stage. Because of her experience in the Radiomics research at Moffitt cancer center, she is the radiologist not only engaged in the imaging diagnosis but also in the computer technology. She aims to use computer analysis-aided diagnosis in the new technology to achieve early detection of Breast cancer. Her continuous research is expected to gain success in the near future to benefit the majority of US women.