Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier

Li, Qingbo; Hao, Chuanyong; Xue, Kang; Zhang, Jialin; Sun, Xuejun; Wang, Wenbo; Zeng, Haishan

doi:10.3390/s17122739

Cited by 21 publications

(15 citation statements)

References 23 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Confirming the results of recent studies [ 47 , 54 ], decreased peaks related to lipids were found at 1742 cm −1 and 1400 cm −1 , and those related to carbohydrates were found at 1160 cm −1 . The increase in the amide I band (1642 cm −1 ) and amide II band (1550 cm −1 ) can be related to changes in the relative amounts of proteins in the cancer cells during cancer progression [ 55 ] and potentially be caused by the unregulated production of cell cytoplasm contents. The decreased peak intensity of the C=O band (1742 cm −1 ) in the cancer sample spectra can be correlated with a higher energy demand during cell proliferation that results in the metabolisation of fats [ 55 ].…”

Section: Resultsmentioning

confidence: 99%

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

Hocotz

Bibikova

Belikova

et al. 2020

Sensors

View full text Add to dashboard Cite

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution from colon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate resection margins to avoid local recurrences due to minimal residual disease. The presence of functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue samples in combination with chemometric models constitutes a promising approach for more efficient and precise tumour margin identification. Additionally, this technique provides a real-time tumour identification approach not only for intraoperative application but also during endoscopic diagnosis of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has advantages compared to individual methods for the clinical implementation of this technique as a diagnostic tool.

show abstract

Section: Resultsmentioning

confidence: 99%

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

Hocotz

Bibikova

Belikova

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…For example, ovarian cancer patients with BRCA mutations or homologous recombination deficiency acquire therapeutic benefits from platinum drugs and PARP inhibitors, while immune checkpoint inhibitors are available for tumors with microsatellite instability. 32 As a convenient, sensitive and rapid technique, FTIR spectroscopy has also been indicated to be valuable and widely used in multiple tumors, such as in the diagnosis of lung, 33 breast, 34 colorectal, 35 , 36 cervical, 37 gastric, 38 liver 39 and thyroid tumors. 40 …”

Section: The Application Of Ftir Spectroscopy In the Diagnosis Of Ovamentioning

confidence: 99%

Fourier Transform Infrared Spectroscopy: An Innovative Method for the Diagnosis of Ovarian Cancer

Yang

et al. 2021

CMAR

View full text Add to dashboard Cite

Ovarian cancer is the most lethal gynecologic malignancy due to the late diagnoses at advanced stages, drug resistance and the high recurrence rate. Thus, there is an urgent need to develop new techniques to diagnose and monitor ovarian cancer patients. Fourier transform infrared (FTIR) spectroscopy has great potential in the diagnosis of this disease, as well as the real-time monitoring of cancer development and chemoresistance. As a noninvasive, simple and convenient technique, it can not only distinguish the molecular differences between normal and malignant tissues, but also be used to identify the characteristics of different types of ovarian cancer. FTIR spectroscopy is also widely used in monitoring cancer cells in response to antitumor drugs, distinguishing cells in different growth states, and identifying new synthetic drugs. In this paper, the applications of FTIR spectroscopy for ovarian cancer diagnosis and other works carried out so far are described in detail.

show abstract

“…Zhi et al 21 used a support vector machine (SVM) classifier for the prediction of the metastasis of CRC. Li et al 24 used an improved k-nearest neighbors classifier to diagnose CRC and colitis with Fourier transform infrared spectroscopy. Long et al 25 examined the random forest, logistic regression, naïve Bayes, and k-nearest neighbors models and multi-platform transcriptomics to introduce novel signatures for the accurate diagnosis of CRC.…”

Section: Introductionmentioning

confidence: 99%

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Lin

Xiao

et al. 2021

Technol Cancer Res Treat

View full text Add to dashboard Cite

Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.

show abstract

Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier

Cited by 21 publications

References 23 publications

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

Fourier Transform Infrared Spectroscopy: An Innovative Method for the Diagnosis of Ovarian Cancer

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Contact Info

Product

Resources

About