Cancer Detection Techniques Research Articles

Bladder Adenocarcinoma in a Constellation of Multiple Site Malignancies: An Unusual Case and Systematic Review

Background and Objectives: Multiple primary malignant tumors represent a small percentage of the total number of oncological cases and can involve either metachronous or synchronous development and represent challenges in diagnosis, staging, and treatment planning. Our purpose is to present a rare case of bladder adenocarcinoma in a female patient with multiple primary malignant tumors and to provide systematic review of the available literature. Materials and Methods: A 67-year-old female patient was admitted with altered general condition and anuria. The past medical history of the patient included malignant melanoma (2014), cervical cancer (2017), colon cancer (2021), obstructive anuria (2023), and liver metastasectomy (2023). Transurethral resection of bladder tumor was performed for bladder tumors. Results: Contrast CT highlighted multiple pulmonary metastases, a poly nodular liver conglomerate, retroperitoneal lymph node, II/III grade left ureterohydronephrosis, and no digestive tract tumor masses. The pathological result of the bladder resection showed an infiltrative adenocarcinoma. Conclusions: The difference between primary bladder adenocarcinoma tumor and metastatic colorectal adenocarcinoma is the key for the future therapeutic strategy. Identification and assessment of risk factors such as viral infection, radiotherapy, chemotherapy, smoking, and genetics are pivotal in understanding and managing multiple primary malignant tumors. Personalized prevention strategies and screening programs may facilitate the early detection of these tumors, whether synchronous or metachronous. The use of multicancer early detection (MCED) blood tests for early diagnosis appears promising. However, additional research is needed to standardize these techniques for cancer detection.

KI-gestützte Atemgasanalyse (E-Nose) zur Früherkennung von Lungenkarzinomen

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

HGLeNet DEEP LEARNING FOR SKIN CANCER DETECTION WITH SKIN LESION SEGMENTATION

Skin cancer is deemed to be the most dangerous type of cancer. It occurs due to the damage caused to the DNA and if left untreated can lead to death. Various methods have been devised over the last few years for skin cancer detection. However, their performance was affected by various challenges in image analysis, like color illumination variations, differences in shape, size, etc. Therefore, to tackle these issues the novel framework, deep learning (DL) technique for accurate skin cancer detection and lesion segmentation is developed. Primarily, the skin image is pre-processed by employing a weighted median filtering to eradicate the noises contained. Then, the segmentation of skin lesions is carried out by employing the efficient neural network (ENet). After that, augmentation is accomplished to avoid overfitting, and later, feature extraction is carried out. At last, skin cancer detection is effectuated with the help of hybrid GoogleNet-LeNet (HGLeNet), which is obtained by merging GoogleNet and LeNet, here layers are modified using the regression concept. Furthermore, the introduced framework is examined for its effectiveness through accuracy, sensitivity and specificity. Moreover, HGLeNet attained the highest accuracy of 0.922, sensitivity of 0.928, as well as specificity of 0.924.

Enhanced pre-processing technique for histopathological image stain normalization and cancer detection

Enhanced pre-processing technique for histopathological image stain normalization and cancer detection

A review of label-free photonics-based techniques for cancer detection in the digestive and urinary systems

Abstract For a long time, it has been known that optics can provide a broad range of tools for addressing clinical needs, particularly diagnostics. Optical techniques can help in identifying diseases and detecting pathological tissues with non/minimally invasive and label-free methods. Given the current limitations of standard clinical procedures, such an approach could provide a powerful tool in detecting gastrointestinal and bladder cancers. However, each technique has serious limitations regarding one or more of the following features: biomarker sensitivity, penetration depth, acquisition times, or adaptation to the clinical environment. Hence there is an increasing need for approaches and instruments based on the concept of multimodality; in this regard, we review the application of different imaging/spectroscopy tools and methods operating in the first two optical windows (SHG, SPEF, TPEF, THG, 3PEF, CARS, Raman and reflectance) for tumour detection in the digestive and urinary systems. This article also explores the possibility of exploiting the third bio-tissue transmission window (1600-1900 nm) by reviewing state of the art in ultrafast laser sources development. Finally, we summarise the most recent results in developing multiphoton endoscopes – a key element for clinical in vivo translation of photonics-based diagnostics.

Survey of AI-driven techniques for ovarian cancer detection: state-of-the-art methods and open challenges

Survey of AI-driven techniques for ovarian cancer detection: state-of-the-art methods and open challenges

Liquid Biopsy in Cancer Diagnosis-Treatment Methodology and Applications

The comprehensive review of liquid biopsy as a cutting-edge, non-invasive technique for cancer detection, diagnosis, and monitoring may be found in the document “Liquid Biopsy in Cancer Diagnosis – Treatment Methodology and Applications.” Via liquid biopsy, tumor-related indicators present in physiological fluids such as blood, urine, and saliva are examined, including exosomes, circulating tumor cells (CTCs), and circulating tumor DNA (ctDNA). Liquid biopsy enables regular, real-time sampling of tumor dynamics, in contrast to standard tissue biopsies, which are invasive and restricted to particular tumor regions. This renders it a potent instrument for the identification of cancer mutations and the tracking of tumor progression, particularly in cases of metastatic or elusive cancers. Technological developments like digital droplet PCR (ddPCR) and next-generation sequencing (NGS) have greatly improved the sensitivity and precision of ctDNA detection, making it possible to identify unusualexceptionally rare genetic mutations. This enhances the efficacy of tailored cancer treatment and monitoring. The genetic heterogeneity of cancers is captured by liquid biopsies, providing information on several tumor subclones that may be missed by traditional biopsies. The document also describes the use of different body fluids for different forms of cancer, such as head and neck and urological tumors, such as saliva, urine, and blood. Blood is the most often utilized medium for tracking genetic changes due to its ease of accessibility. Saliva and urine are useful in the detection of head and neck malignancies and bladder cancer, respectively. Liquid biopsy has several advantages, but it also has drawbacks, such as the requirement for standardized procedures, high prices, and differences in sensitivity and specificity between platforms.In order to overcome these obstacles and integrate liquid biopsy into standard clinical practice, the paper emphasizes the significance of more research and development. This will improve patient outcomes, early cancer detection, and personalized treatment.

Cancer Detection and Treatment Using Explainable AI

Cancer detection and treatment is one of the most difficult tasks in modern medicine, hence it has become a priority for researchers to study and develop specific and targeted procedures to patient outcomes. Just recently, the most promising direction is the humanization of explainable AI (XAI) which is a crucial tool for enhancing AI-based decision making in terms of transparency, and understandability. This paper starts out by describing conventional AI techniques for cancer detection and pointing out the informational gaps then delves into XAI's foundations. It explores the role of XAI in cancer detection and treatment by looking at its potential impact on this topic. It first presents the diagnosis of medical imaging data, then a discussion how patients' data will be interpreted by a XAI system and how treatment strategies that are tailored to the individual patient will be developed by optimizing the therapeutic interventions. This paper also presents comprehensive study of the changing side cancer treatment rendered by XAI and explain why further research and cooperation are getting to be of paramount importance in order for it to be fully exploited. Implementation of XAI in screening and treatment techniques for cancer comes to the conclusion of not only revolutionizing precision medicine, but also aiding in the improvement of patient care and will determine oncology practice in future.

Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

The role of physical therapists in the early detection of skin melanoma: insights from an anonymous survey.

Skin melanoma is the most severe form of skin cancer. Recent years have seen an increase in melanoma incidence. Melanomas often appear on the back, a hidden area, leading to late diagnoses. Physical therapists, who frequently treat patients with lower back pain, could play a crucial role in early melanoma detection. An anonymous online survey was conducted among 254 Israeli physical therapists to assess their perspectives on melanoma detection, their knowledge in identifying suspicious lesions, and their referral patterns. The survey included sections on demographics, professional perspectives, melanoma knowledge, personal or family experiences with melanoma, and referral frequency for dermatological evaluation. The survey revealed that 75.2% of physical therapists viewed melanoma detection as part of their professional duties, yet 59.1% reported insufficient knowledge in identifying suspicious lesions. Despite this, 94.1% expressed a desire to improve their knowledge. However, only 44.1% actively referred patients with suspicious lesions to dermatologists. There was a significant positive correlation between professional experience and referral rates (p-value < 0.001), indicating that more experienced therapists were more likely to refer patients. The study highlights the critical need for incorporating melanoma detection training into physical therapy education and professional development. Enhancing physical therapists' knowledge and skills in this area could improve early detection and patient outcomes. Despite the potential role of physical therapists in melanoma detection, current training programs lack emphasis on this aspect, underscoring the importance of revising educational curricula to include skin cancer detection techniques. The findings suggest that while physical therapists recognize their role in melanoma detection, there is a significant knowledge gap. Addressing this through targeted education and training could enhance early detection efforts and improve patient care.

Improved rank-based recursive feature elimination method based ovarian cancer detection model via customized deep architecture

BackgroundOvarian cancer is often considered the most lethal gynecological cancer because it tends to be diagnosed at an advanced stage, leading to limited treatment options and poorer outcomes. Several factors contribute to the challenges in managing ovarian cancer, namely rapid metastasis, genetic factors, reproductive history, etc. This necessitates the prompt and precise diagnosis of ovarian cancer in order to carry out efficient treatment plans and give patients who are all impacted by OC the care and support they need. MethodsThis CCLSTM model is suggested under four essential stages including preprocessing, feature extraction, feature selection and detection. Initially, the input data is preprocessed using Improved Two-step Data Normalization. Subsequently, features such as statistical, modified entropy, raw features and mutual information are extracted from the normalized data. Next, obtained features undergo the Improved Rank-based Recursive Feature Elimination method (IR-RFE) to select the most suitable features. Finally, the proposed CCLSTM model takes the selected features as input and provides a final detection outcome. ResultsFurthermore, the performance of the proposed CCLSTM technique is examined through a thorough assessment using diverse analyses Additionally, the CCLSTM schemes show a sensitivity value of 0.948, whereas the sensitivity ratings for ALO-LSTM + ALOCNN, Bi-GRU, LSTM, RNN, KNN, CNN, and DCNN are 0.808, 0.893, 0.829, 0.851, 0.765, 0.872, and 0.893, respectively. ConclusionIn the end, the development of CNN and the addition of LSTM technique have produced an ovarian cancer detection technique that is more accurate and consistent compared to other existing strategies.

Advanced optical imaging techniques for bladder cancer detection and diagnosis: a systematic review.

To systematically assess the current available literature concerning advanced optical imaging methods for the detection and diagnosis of bladder cancer (BCa), focusing particularly on the sensitivity and specificity of these techniques. First a scoping search was performed to identify all available optical techniques for BCa detection and diagnosis. The optical imaging techniques used for detecting BCa are: the Storz professional image enhancement system (IMAGE1 S), narrow-band imaging (NBI), photoacoustic imaging (PAI), autofluorescence imaging (AFI), photodynamic diagnosis (PDD), and scanning fibre endoscopy (SFE). The staging and grading techniques for BCa are: optical coherence tomography (OCT), confocal laser endomicroscopy (CLE), Raman spectroscopy, endocytoscopy, and non-linear optical microscopy (NLO). Then a systematic literature search was conducted using MEDLINE, EMBASE and Web of Science from inception to 21 November 2023. Articles were screened and selected by two independent reviewers. Inclusion criteria were: reporting on both the sensitivity and specificity of a particular technique and comparison to histopathology, and in the case of a detection technique comparison to white light cystoscopy (WLC). Out of 6707 articles, 189 underwent full-text review, resulting in 52 inclusions. No articles met criteria for IMAGE1 S, PAI, SFE, Raman spectroscopy, and endocytoscopy. All detection techniques showed higher sensitivity than WLC, with NBI leading (87.8-100%). Overall, detection technique specificity was comparable to WLC, with PDD being most specific (23.3-100%). CLE and OCT varied in sensitivity and specificity, with OCT showing higher specificity for BCa diagnosis, notably for carcinoma in situ (97-99%) compared to CLE (62.5-81.3%). NLO demonstrated high sensitivity and specificity (90-97% and 77-100%, respectively) based on limited data from two small ex vivo studies. Optical techniques with the most potential are PDD for detecting and OCT for staging and grading BCa. Further research is crucial to validate their integration into routine practice and explore the value of other imaging techniques.

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.

The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.

Advanced Machine Learning Techniques for Early Breast Cancer Detection through DNA Methylation Analysis

Advanced Machine Learning Techniques for Early Breast Cancer Detection through DNA Methylation Analysis

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensive analysis of Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self- learning and decision-making.

Identifying gastric cancer by optical coherence tomography based on an optimized depth-resolved attenuation estimation

Optical coherence tomography (OCT) is a reliable technique for cancer detection, with the potential to improve accuracy in identifying cancerous tissue through effective use of OCT image data. We proposed an optimized depth resolution estimation based optical attenuation coefficient (OAC) mapping method to reduce the error effect caused by discretization without considering whether the light is completely attenuated or not. The attenuation coefficient maps of gastric tissue were reconstructed using this method. Features were extracted from these maps using gray-level co-occurrence matrix and gray histogram, and a support vector machine was employed as a classifier for identifying gastric cancer tissues. The recognition accuracy achieved was 98.60%, which was higher than the 94.30% accuracy in the control group without OACs. This approach enhances the utilization of OCT image information and improves its practical application ability by increasing the recognition accuracy.

AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer.

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

Colon Cancer Detection of Colonoscopy Images Using CNN

Abstract: Colon cancer requires an early and precise diagnosis in order to be effectively treated because it is a common and potentially fatal condition. This study proposes a reliable framework for colon cancer detection through image preprocessing and transfer learning approaches. The research compares the performance of three popular convolutional neural networks (CNNs) for this task: VGG-15, ResNet-50, and AlexNet.The image preprocessing phase entails a number of critical operations, including image conversion, resizing, and contrast enhancement using histogram equalization. In order to guarantee that the neural networks receive high-quality inputs, these preprocessing techniques strive to improve the quality and consistency of the colon cancer dataset. Pre-trained models extract and classify features from big datasets using transfer learning. The preprocessed colon cancer dataset is used to fine-tune the chosen CNN architectures VGG-15, ResNet-50, and AlexNet and adapt them for this particular medical imaging purpose.The results of this study show that transfer learning has the potential for colon cancer detection and provides helpful direction for researchers and practitioners looking for the best deep-learning architectures for early cancer diagnosis. This study adds to continuing efforts to improve the precision and effectiveness of colon cancer detection techniques, ultimately leading to better patient outcomes.

Deep learning approaches for breast cancer detection in histopathology images: A review.

Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.