An official website of the United States government
Previously, the analysis of atomic force microscopy (AFM) images allowed us to distinguish normal from cancerous/precancerous human epithelial cervical cells using only the fractal dimension parameter. High-resolution maps of adhesion between the AFM probe and the cell surface were used in that study. However, the separation of cancerous and precancerous cells was rather poor (the area under the curve (AUC) was only 0.79, whereas the accuracy, sensitivity, and specificity were 74%, 58%, and 84%, respectively). At the same time, the separation between premalignant and malignant cells is the most significant from a clinical point of view. Here, we show that the introduction of machine learning methods for the analysis of adhesion maps allows us to distinguish precancerous and cancerous cervical cells with rather good precision (AUC, accuracy, sensitivity, and specificity are 0.93, 83%, 92%, and 78%, respectively). Substantial improvement in sensitivity is significant because of the unmet need in clinical practice to improve the screening of cervical cancer (a relatively low specificity can be compensated by combining this approach with other currently existing screening methods). The random forest decision tree algorithm was utilized in this study. The analysis was carried out using the data of six precancerous primary cell lines and six cancerous primary cell lines, each derived from different humans. The robustness of the classification was verified using K-fold cross-validation (K = 500). The results are statistically significant at p < 0.0001. Statistical significance was determined using the random shuffle method as a control.
more »
« less
Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning
The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique’s capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (p < 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection.
more »
« less
- Award ID(s):
- 2224708
- PAR ID:
- 10616983
- Publisher / Repository:
- https://www.mdpi.com/
- Date Published:
- Journal Name:
- Cells
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2073-4409
- Page Range / eLocation ID:
- 14
- Subject(s) / Keyword(s):
- imaging nanomedicine cancer atomic force microscopy ringing mode artificial intelligence machine learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
It has been recently demonstrated that atomic force microscopy (AFM) allows for the rather precise identification of malignancy in bladder and cervical cells. Furthermore, an example of human colorectal epithelial cells imaged in AFM Ringing mode has demonstrated the ability to distinguish cells with varying cancer aggressiveness with the help of machine learning (ML). The previously used ML methods analyzed the entire cell image. The problem with such an approach is the lack of information about which features of the cell surface are associated with a high degree of aggressiveness of the cells. Here we suggest a machine-learning approach to overcome this problem. Our approach identifies specific geometrical regions on the cell surface that are critical for classifying cells as highly or lowly aggressive. Such localization gives a path to colocalize the newly identified features with possible clustering of specific molecules identified via standard bio-fluorescence imaging. The biological interpretation of the obtained information is discussed.more » « less
-
A novel method based on atomic force microscopy (AFM) working in Ringing mode (RM) to distinguish between two similar human colon epithelial cancer cell lines that exhibit different degrees of neoplastic aggressiveness is reported on. The classification accuracy in identifying the cell line based on the images of a single cell can be as high as 94% (the area under the receiver operating characteristic [ROC] curve is 0.99). Comparing the accuracy using the RM and the regular imaging channels, it is seen that the RM channels are responsible for the high accuracy. The cells are also studied with a traditional AFM indentation method, which gives information about cell mechanics and the pericellular coat. Although a statistically significant difference between the two cell lines is also seen in the indentation method, it provides the accuracy of identifying the cell line at the single‐cell level less than 68% (the area under the ROC curve is 0.73). Thus, AFM cell imaging is substantially more accurate in identifying the cell phenotype than the traditional AFM indentation method. All the obtained cell data are collected on fixed cells and analyzed using machine learning methods. The biophysical reasons for the observed classification are discussed.more » « less
-
Peptide-Mediated Targeting Mesoporous Silica Nanoparticles: A Novel Tool for Fighting Bladder CancerTransitional cell carcinoma of the bladder is particularly devastating due to its high rate of recurrence and difficulty in retention of treatments within the bladder. Current cystoscopic approaches to detect and stage the tumor are limited by the penetrative depth of the cystoscope light source, and intravesical dyes that highlight tumors for surgical resection are non-specific. To address the needs for improved specificity in tumor detection and follow-up, we report on a novel technology relying on the engineered core of mesoporous silica (MSN) with surface modifications that generate contrast in fluorescence and magnetic resonance imaging (MRI). The particle surface was further functionalized to include a bladder cancer cell specific peptide, Cyc6, identified via phage display. This peptide possesses nanomolar specificity for bladder cancer cells and homology across multiple species including mouse, canine, and human. Our study takes advantage of its target expression in bladder tumor which is not expressed in normal bladder wall. When functionalized to MSN, the Cyc6 improved binding efficiency and specificity for bladder cancer cells in vitro. In an in vivo model, MSN instilled into bladders of tumor-bearing mice enhanced T 1- and T 2-weighted MRI signals, improving the detection of the tumor boundaries. These findings support the notion that our targeted nanomaterial presents new options for early detection and eventual therapeutic intervention. Ultimately, the combination of real-time and repeated MRI evaluation of the tumors enhanced by nanoparticle contrast have the potential for translation into human clinical studies for tumor staging, therapeutic monitoring, and drug delivery.more » « less
-
Abstract— Recent advances show the wide-ranging applications of machine learning for solving multi-disciplinary problems in cancer cell growth detection, modeling cancer growths and treatments, etc. There is growing interests among the faculty and students at Clayton State University to study the applications of machine learning for medical imaging and propose new algorithms based on a recently funded NSF grant proposal in medical imaging, skin cancer detection, and associated smartphone apps and a web-based user-friendly diagnosis interface. We tested many available open-source ML algorithm-based software sets in Python as applied to medical image data processing, and modeling used to predict cancer growths and treatments. We study the use of ML concepts that promote efficient, accurate, secure computation over medical images, identifying and classifying cancer cells, and modeling the cancer cell growths. In this collaborative project with another university, we follow a holistic approach to data analysis leading to more efficient cancer detection based upon both cell analysis and image recognition. Here, we compare ML based software methods and analyze their detection accuracy. In addition, we acquire publicly available data of cancer cell image files and analyze using deep learning algorithms to detect benign and suspicious image samples. We apply the current pattern matching algorithms and study the available data with possible diagnosis of cancer types.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/cells14010014
