Supportive Noninvasive Tool for the Diagnosis of Breast Cancer Using a Thermographic Camera as Sensor

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital

Objective

Artificial intelligence-enhanced breast thermography is being evaluated as an ancillary modality in the evaluation of breast disease. The objective of this study was to evaluate the clinical performance of Thermalytix, a CE-marked, AI-based thermal imaging test, with respect to conventional mammography.

Methods

A prospective, comparative study performed between 15 December 2018 and 06 January 2020 evaluated the performance of Thermalytix in 459 women with both dense and nondense breast tissue. Both symptomatic and asymptomatic women, aged 30-80 years, presenting to the hospital underwent Thermalytix followed by 2-D mammography and appropriate confirmatory investigations to confirm malignancy. The radiologist interpreting the mammograms and the technician using the Thermalytix tool were blinded to the others' findings. The statistical analysis was performed by a third party.

Results

A total of 687 women were recruited, of whom 459 fulfilled the inclusion criteria. Twenty-one malignancies were detected (21/459, 4.6%). The overall sensitivity of Thermalytix was 95.24% (95% CI, 76.18-99.88), and the specificity was 88.58% (95% CI, 85.23-91.41). In women with dense breasts (n = 168, 36.6%), the sensitivity was 100% (95% CI, 69.15-100), and the specificity was 81.65% (95% CI, 74.72-87.35). Among these 168 women, 37 women (22%) were reported as BI-RADS 0 on mammography; in this subset, the sensitivity of Thermalytix was 100% (95% CI, 69.15-100), and the specificity was 77.22% (95% CI, 69.88-83.50).

Conclusion

Thermalytix showed acceptable sensitivity and specificity with respect to mammography in the overall patient population. Thermalytix outperformed mammography in women with dense breasts and those reported as BI-RADS 0.

Quality analysis of a breast thermal images database

The study and early detection of breast cancer are key for its treatment. We carry out an exhaustive analysis of the most used database for mastology research with infrared images, analyzing the anomalies according to five quality dimensions: completeness, correctness, concordance, plausibility, and currency. We established control queries that looked for these anomalies and that can be used to ensure the quality of the database. Finally, we briefly review the more than 40 papers that use this database and that do not mention any of these anomalies. When analyzing the database, we found 365 anomalies related to personal and clinical data, and thermal images. The errors found in our research may lead to a modification of the results and conclusions made in the articles found in the literature, serve as a basis for improvements in the quality of the database, and help future researchers to work with it.

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

Feasibility for Using Thermography Throughout an Exercise Program in Mastectomized Patients

Introduction

Breast cancer is the most common in the female population. Physical training is safe and indicated after surgical treatment for breast cancer. During exercise, body temperature changes due to tissue metabolic activity; in this sense, infrared thermography is used to map the thermal patterns of the body surface.

Objective

This study aimed to evaluate the feasibility of using thermography during a physical rehabilitation program in mastectomized patients by analyzing the change in body temperature caused by physical exercise in the breast region.

Methodology

This is a simple and covert clinical trial, in which the sample was constituted for convenience. The women were submitted to a supervised physical exercise protocol, three times a week, for 20 sessions. They were evaluated in the first, tenth, and twentieth sessions in relation to changes in body temperature in the breast region (infrared thermography).

Results

Twenty patients who underwent mastectomy surgery were recruited. No patient had drain infection, scar dehiscence, or lymphedema, and only one patient had seroma removed. The mean age was 50.45 ± 2.00 years, and the body mass index (BMI) was 28.95 ± 1.11 kg/m2. In the body thermography of the patients’ breast region, no significant difference was observed when comparing the thermograms of the plastron region of the patients in the first, tenth, and twentieth sessions (p = 0.201). However, when comparing the plastron region with the control breast, a reduction in temperature was observed in the operated region in the first (p = 0.012) and tenth sessions (p = 0.004).

Conclusion

Through this study, we can conclude that the use of infrared thermography is viable for the analysis of the body temperature of mastectomized patients during a supervised physical exercise protocol and, therefore, suggest that this instrument is increasingly used in the cancer public.

BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network

The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its implementation framework. BreaCNet consists of an effective segmentation algorithm for breast thermograms and a classifier based on the mobile CNN model. The segmentation algorithm employing edge detection and second-order polynomial curve fitting techniques can effectively capture the thermograms' region of interest (ROI), thereby facilitating efficient feature extraction. The classifier was developed based on ShuffleNet by adding one block consisting of a convolutional layer with 1028 filters. The modified Shufflenet demonstrated a good fit learning with 6.1 million parameters and 22 MB size. Simulation results showed that modified ShuffleNet alone resulted in a 72% accuracy rate, but the performance excelled to a 100% accuracy rate when integrated with the proposed segmentation algorithm. In terms of diagnostic accuracy of the normal and abnormal test, BreaCNet significantly improves the sensitivity rate from 43% to 100% and specificity of 100%. We confirmed that feeding only the ROI of the input dataset to the network can improve the classifier's performance. On the implementation aspect of BreaCNet, the on-device inference is recommended to ensure users' data privacy and handle an unreliable network connection.

The Efficacy of Photobiomodulation Therapy in Improving Tissue Resilience and Healing of Radiation Skin Damage

The increased precision, efficacy, and safety of radiation brachytherapy has tremendously improved its popularity in cancer care. However, an unfortunate side effect of this therapy involves localized skin damage and breakdown that are managed palliatively currently. This study was motivated by prior reports on the efficacy of photobiomodulation (PBM) therapy in improving tissue resilience and wound healing. We evaluated the efficacy of PBM therapy on 36 athymic mice with 125I seed (0.42 mCi) implantation over 60 days. PBM treatments were performed with either red (660 nm) or near-infrared (880 nm, NIR) LEDs irradiance of 40 mW/cm2, continuous wave, fluence of 20 J/cm2 once per week. Animals were evaluated every 7 days with digital imaging, laser Doppler flowmetry, thermal imaging, µPET-CT imaging using 18F-FDG, and histology. We observed that both PBM treatments—red and NIR—demonstrated significantly less incidence and severity and improved healing with skin radionecrosis. Radiation exposed tissues had improved functional parameters such as vascular perfusion, reduced inflammation, and metabolic derangement following PBM therapy. Histological analysis confirmed these observations with minimal damage and resolution in tissues exposed to radiation. To our knowledge, this is the first report on the successful use of PBM therapy for brachytherapy. The results from this study support future mechanistic lab studies and controlled human clinical studies to utilize this innovative therapy in managing side effects from radiation cancer treatments.

LINPE-BL: A Local Descriptor and Broad Learning for Identification of Abnormal Breast Thermograms

This paper proposes a novel local feature descriptor coined as a local instant-and-center-symmetric neighbor-based pattern of the extrema-images (LINPE) to detect breast abnormalities in thermal breast images. It is a hybrid descriptor that combines two different feature descriptors: one is the inverse-probability difference extrema (IpDE), and another is the local instant and center-symmetric neighbor-based pattern (LICsNP). IpDE is developed to compute the intensity-inhomogeneity-invariant feature-based image of the breast thermogram. Besides, the LICsNP is intended to capture the local microstructure pattern information in the IpDE image. A new paradigm, named Broad Learning (BL) network, is introduced here as a classifier to differentiate the healthy and sick breast thermograms efficiently. The efficacy of the proposed system is quantitatively validated on the images of DMR-IR and DBT-TU-JU databases. Extensive experimentation on these databases with an average accuracy of 96.90% and 94%, respectively, justifies proposed system's superiority in the differentiation of healthy and sick breast thermograms over the other related existing state-of-the-art methods. The proposed system also performs consistently in the presence of noise and rotational changes.

Highly Discriminative Physiological Parameters for Thermal Pattern Classification

Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physiological parameters are estimated through the inverse solution of the bio-heat equation and the STD of suspicious areas related to the hottest spots of the RoI. To reach these values, the STD is analyzed by means: the Depth-Intensity-Radius (D-I-R) measurement model and the fitting method of Lorentz curve. A highly discriminative pattern vector composed of the extracted physiological parameters is proposed to classify normal and abnormal breast thermograms. A well-defined RoI is delimited at a radial distance, determined by the Support Vector Machines (SVM). Nevertheless, this distance is less than or equal to 1.8 cm due to the maximum temperature location close to the boundary image. The methodology is applied to 87 breast thermograms that belong to the Database for Mastology Research with Infrared Image (DMR-IR). This methodology does not apply any image enhancements or normalization of input data. At an optimal position, the three-dimensional scattergrams show a correct separation between normal and abnormal thermograms. In other cases, the feature vectors are highly correlated. According to our experimental results, the proposed pattern vector extracted at optimal position a=1.6 cm reaches the highest sensitivity, specificity, and accuracy. Even more, the proposed technique utilizes a reduced number of physiological parameters to obtain a Correct Rate Classification (CRC) of 100%. The precision assessment confirms the performance superiority of the proposed method compared with other techniques for the breast thermogram classification of the DMR-IR.

[Connected bras for breast cancer detection in 2021: Analysis and perspectives]

OBJECTIVES

Breast cancer is the leading cancer in women worldwide with about 2 million new cases and 685,000 deaths each year. Mammography is the most widely used screening and diagnostic method. Currently, digital technologies advances facilitate the development of connected and portable devices. To overcome some of the disadvantages of mammography (breast compression, difficulty in analyzing dense breasts, radiation, limited accessibility in some countries, etc.), portable devices, conventionally known as connected bras (CB), have been created to offer an alternative method to mammography. The objective of our review was to list all the published CBs in order to know their main characteristics, their potential indications and their possible limitations.

METHOD

A bibliographical search in the PUBMED database selecting only articles written in French or English, between 2011 and 2020, found 7 CBs under development.

RESULTS

These CBs use thermal, ultrasonic and impedance sensors. Their advantages are an absence of irradiation, an absence of breast compression and a flexibility of use (outside an X-ray cabinet). Mammary gland analysis times vary, depending on the device, between 30min and 24h. They are all connected to data transmission systems and models that analyze the results.

DISCUSSION AND CONCLUSION

These CBs are mostly still undergoing clinical validation (only [iTBra] has been evaluated in a clinical trial) and require evaluation steps that will eventually allow their future use for breast cancer detection in high-risk women, particularly in women with dense breasts and in women between screening waves.

Autumnal Beach Litter Identification by Mean of Using Ground-Based IR Thermography

The progress of scientific research and technological innovation are contributing to an increase in the use of rapid systems for monitoring and identifying geo-environmental processes related to natural and/or anthropogenic activities. The aim of this study is identifying autumnal beach litter using ground-based IR thermography. Starting from quarterly autumn monitoring data of air temperature and sandy soil surface temperature, an empirical equation between the two environmental matrices (air and sandy soil) is obtained. This will allow the calculation of the sandy soil surface temperature knowing only the air temperature. Therefore, it will be possible to know in advance the thermal response of the sandy soil, thus creating a thermal blank of the beach. Using an IR thermal camera, it is possible for a quicker identification of thermal anomalies of the coastal area potentially connected to the presence of pollution due to the anthropogenic origin (particularly plastic material). The test area is located in the area of the Coastal Dunes Regional Natural Park of Ostuni–Fasano in Apulia (southern Italy).

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review

Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body. Thermography as a screening tool was first introduced and well accepted in 1956. However, a study in 1977 found that it lagged behind other screening tools and is subjective. Soon after, its use was discontinued. This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings. With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade. This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence. However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively. Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women.

A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection

Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.

Risk of breast cancer based on thermal tomography characteristics

Background

There is no uniform standard for the diagnosis of breast lesions by thermal tomography (TT). This study aimed to widely analyse the predictive value of TT in patients with breast cancer and establish a uniform standard for the diagnosis of breast lesions.

Methods

We retrospectively analysed data from women who suffered from non-inflammatory unilateral single breast lesion and underwent TT from January 2014 to July 2016. Changes in TT parameters were correlated with the pathologic diagnosis, and its predictive value was assessed.

Results

A total of 407 patients underwent TT examinations during the study period, including 196 subsequently diagnosed with breast cancer. Several characteristics were found to be significantly correlated with breast cancer: age ≥60 years [odds ratio (OR) =109.296, P<0.001], age ≥35 and <60 years (OR =25.720, P<0.001), q-r curve as an angle of 30°–45° (OR =14.895, P<0.001), ΔTs (surface temperature difference between the neoplastic side and the healthy side) ≥0.65 °C (OR =4.129, P<0.001), ΔTn (nipple temperature difference between the neoplastic side and the healthy side) ≥0.45 °C (OR =2.683, P=0.006), isotherm asymmetry (OR =2.297, P=0.035), and vascular plentiful (OR =3.333, P=0.004). Q value as a novel predictive indicator based on the multiple predictor modelling improved the diagnostic rate for breast cancer, and the accuracy in this study was up to 86.7%.

Conclusions

Age, q-r curve, ΔTs, ΔTn, isotherm, and vascular features were independent predictors of breast cancer. Q value could be used to assess the risk of breast cancer as an additional diagnostic tool for breast cancer screening and diagnosis.

Infrared Thermography-Based Breast Cancer Detection — Comprehensive Investigation

Breast cancer has been reported to be the first deadly disease that affects women worldwide. This type of cancer has been reported to be the second leading cause of death in women worldwide. Medical reports have also reported that every woman is exposed to having breast cancer with an average probability of about 12%. It has also been reported to be the most common cancer that affects women. Fatality could be due to the cancer detection delay; in other words, early detection of the tumor can increase the survival rate of patients. Routine techniques of imaging modalities for cancer screening such as Mammography, Computated Tomography (CT) scan, Magnetic Resonance Imaging (MRI) and ultrasound are impractical tools for many reasons such as the irreproducible nature, the high error rate in cases of thick breasts, the pain and the annoyance they cause. Consequently, there is a need for more convincing strategies with high accuracy rates in breast cancer detection. Therefore, among the large variety of medical breast scanning techniques, thermography has attracted attention in applications related to detection and diagnosis. It is capable of providing helpful and useful information about the physiological variations and accordingly, it can detect tumors even in early stages. In addition, it is a very safe scanning tool, so as many needed tests can be held in proper time and manner. Thermography relies on the fact that human body temperature generally is a natural norm for the diagnosis of diseases. Thermography in medical applications applies infrared body examination tool which is fast, noninvasive, noncontact, pain free, radiation free and flexible to monitor the temperature of the human body. The fundamental principle of thermography relies on physiology such as the distribution of temperature on the skin surface. Infrared thermography scanning for breasts is an imaging technique which essentially searches for temperature change in human body. Temperature variance could be considered as a good indicator of tumor occurrence in the scanned area. Tumor mainly causes a noteworthy increase in blood vessel circulation and metabolic activity, so it causes higher radiations emitted from the human body around the regions of tumor. The paper surveys the literature work conducted in the field of breast cancer detection from thermogram scans. The survey is followed by a discussion of the strengths and weaknesses of thermography-based tumor detection. A new research idea and some considerations are then suggested based on that discussion to achieve better results in this critical area.