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Breast Cancer Prediction for Improved Diagnosis
medical

Breast Cancer Prediction for Improved Diagnosis

Breast cancer prediction is a diagnosis tool. Oncologists and medical staff face the challenge of identifying breast cancer as soon as possible. An early diagnosis increases survival chances. TADA can help them diagnose malignant tumours quicker.

Industry

Healthcare

Project Duration and Effort

One week

Type of Prediction

Binary Classification

Customer Benefits

  1. 96% accuracy in identifying cancer-causing cell nuclei with TADA versus 79% by clinicians.
  2. No need to be an experienced physician, substantial accuracy available for senior and junior physicians alike.
  3. Speed, once the tool is in place, TADA’s analysis takes a few minutes.
  4. TADA improves early cancer detection by 17%.

Problem to solve

Breast cancer is one of the most common cancers today in women. A breast mass in patients means a tumour. It does not necessarily imply a malignant one. Hence, American oncologists perform a fine needle aspirate (FNA) on the cancer patient.

Then, they examine the resulting cells by microscopy and extract the cells’ nuclei features from the histopathological image. The most critical step is this feature extraction.

The goal is to select elements of this image that one can measure for further computational analysis. At the same time, the measures should be representative of cancer. We aim to use elements of the image measured as either a diagnostic or a prognostic indicator. The early detection of cancer is an efficient means to improve the patient’s survival rate.

The typical features extracted are:

  • Radius
  • Texture
  • Perimeter
  • Area
  • Smoothness
  • Compactness
  • Concavity (severity of concave portions of the contour)
  • Concave points (number of concave portions of the contour)
  • Cymmetry
  • Fractal dimension

Several figures are computed for each feature.

The diagnosis of cancer has been mostly dependent on traditional approaches, using trained professionals’ expertise. However, a senior trained professional is not always available.

TADA’s Machine Learning approach can help automate, in part, the cancer risk prediction. Thus senior and junior professionals alike get access to the same analyzed data from cancer patients. They can provide a better, quicker diagnosis, hence improving survival rates.

Objectives

  • Make the distinction between benign and malignant tumours after an FNA.
  • Support, improve, and reassure oncologists in their diagnoses.
  • Improve the accuracy of breast cancer prediction.

 

It poses the following oncology question:

Can cancer prediction distinguish malignant from benign tumours?

Solution

The TADA predictive models’ results reach a 96% accuracy, a 92% MCC and a 99% AUC based on real data for breast cancer prediction. Breast cancer is the most common cancer among women, accounting for 25% of all cancer cases worldwide. It affects 2.1 million people yearly. Early diagnosis through breast cancer prediction significantly increases the chances of survival.

A Fine Needle Aspiration biopsy (FNA) is a biopsy that produces a fast, reliable, and economic evaluation of tumour lesions. It is a minimally invasive scheme that utilizes a fine needle to aspirate tissue from mass lesions. It expedites the sequence between the diagnosis and the beginning of therapy for breast cancer.

FNA is ideally conducted by an expert medical biologist who can follow with prompt microscopic examination. Research indicates that the most experienced physicians can diagnose breast cancer using FNA with a 79% accuracy.

TADA has selected the following four main criteria out of the thirty available in the dataset. They approximately bear the same weight in the decision to identify breast cancer:

  1. The worst radius,
  2. The worst number of concave points around,
  3. The contour,
  4. The radius standard deviation,
  5. The smoothness.
TADA improves breast cancer predictions by 17% (from 79% to 96%). The artificial intelligence tool distinguishes benign from malignant tumours by providing a probability of the tumour being one or the other. It can also help the oncologist understand how each element measured impacts the diagnosis. For instance, it can prove the relationship between the tumour’s overall dimension and breast cancer chances.

Live Predict

Customer Benefits

In one week, oncologists gained significant support in their cancer diagnosis and their fight against breast cancer by:

  • Distinguishing between benign and malignant cancer quickly.
  • Diagnosing malignant cancers with 96% accuracy.
  • Obtaining an immediate “what-if” analysis linking the tumour’s characteristics with cancer.