A Noninvasive Screening Tool for Breast Cancer: Computerized Detection of Subtle Changes in Mammograms

This week, IBM released the world’s first noninvasive screening tool to detect subtle changes in mammograms called Computerized Detection for Subtle Changes (CD4SC). 

The software has been designed to address the significant limitations of current computer-aided diagnosis (CAD) systems and is expected to provide a more nuanced and comprehensive analysis of mammograms without the need for invasive and expensive biopsies.

The CD4SC system is based on a video game-like test 

It helps doctors better understand which areas on a woman’s mammogram need closer examination. 

Here at https://thehitmensite.com/ has some more information about computerized detection of subtle changes in mammograms.

A variety of computer-aided diagnosis approaches have been used to detect cancer in mammograms. 

CD4SC not only complements these systems but also extends the abilities of the current crop of cancer detection technologies by enabling clinicians to visually detect subtle changes in a mammogram. 

CD4SC will allow a clinician to evaluate a patient’s mammogram and ask the computer to identify which areas need closer examination. 

Conversely, if a particular area on a patient’s mammogram does not need closer examination, the software allows the clinician to instruct the system accordingly.

The computerized detection of subtle changes in mammograms is an in-house screening tool for breast cancer. The advantage of the computerized detection is that it does not require invasive surgical procedures to identify cancerous cells in the breast. 

A huge number of people are diagnosed with cancers each year, and many of them are very young. Research shows that not all cancers present visible symptoms before they’ve advanced significantly, which means early diagnosis is key to preventing disease progression. 

More than 97% of detectable cancers can be cured if caught early, making a reliable screening process vitally important for everyone over 40 years old and older women who have a family history or other risk factors for breast cancer development. 

There are several mammography screening tools available, but none of them on the market produce satisfactory results. 

The first method is called enhanced-digital mammography

Which highlights the areas of concern for radiologists to review on the computer screen. The radiologist must mentally process this information and determine if additional images are needed before rendering an opinion on whether or not intervention is necessary. 

This procedure is time consuming and costly, as it requires more images to be taken than normally required for mammograms. Since radiologists still require images to fully assess all abnormal findings, there is an increased amount of radiation exposure to the patient during this procedure. 

Another method is automated detection of calcifications in mammograms.

This procedure, however, is very difficult to automate, as calcifications are detected with human interpretations. There are at most only 50 cases that have been fully automated with this tool. 

The final method uses machine learning algorithms to detect cancerous cells.

The system has already been shown to improve the detection of breast cancers by more than 10%, although it is still not perfect and fails to detect some cancerous cells that are later detected manually by radiologists. 

Computerized detection is a combination of digital image processing techniques using multiple stages of filtering, classification, and modelling techniques on digitized mammogram images. Several mammograms are passed through filters that remove noisy elements in order to isolate the relevant structures of interest. 

Decisions are made based on their location, shape, size, position, and texture of the breast tissue by feature recognition techniques. The computerized detection also involves statistical methods used to reduce or eliminate false positives.

The process is based on collecting mammograms from several patients through a trusted source. The collection includes digitized images of mammography of patients free of cancer lesions and image processing software is used to filter the images of the breasts. 

Machine learning algorithms are used to classify regions of concern according to their appearance and decide whether or not they require further examination by doctors. These regions of concern can be overlaid on top of the original image to allow doctors to examine the mammograms more thoroughly.

The computerized detection was first published by the University of Michigan in 2011. It was then tested and was shown to produce an overall detection rate of 70% for mammograms that had not been flagged by the radiologist as suspicious. 

The system detects subtle changes such as suspect mammogram findings, but it is also able to detect 90% of invasive cancers that are initially missed in the early stages. 

Additionally, this method can be applied to test new mammography machines with little time required.

However, significant variability exists in computerized detection results between different institutions, which has led to some controversy about this new technology.