Abstract-Application of Terahertz Spectroscopy and Imaging in the Diagnosis of Prostate Cancer

Ping Zhang, Shuncong Zhong, Junxi Zhang, Jian Ding, Zhenxiang Liu, Yi Huang, Ning Zhou, Walter Nsengiyumva, and Tianfu Zhang

https://www.osapublishing.org/copp/abstract.cfm?uri=copp-4-1-31

The feasibility of the application of terahertz electromagnetic waves in the diagnosis of prostate cancer was examined. Four samples of incomplete cancerous prostatic paraffin-embedded tissues were examined using terahertz spectral imaging (TPI) system and the results obtained by comparing the absorption coefficient and refractive index of prostate tumor, normal prostate tissue and smooth muscle from one of the paraffin tissue masses examined were reported. Three hundred and sixty cases of absorption coefficients from one of the paraffin tissues examined were used as raw data to classify these three tissues using the Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LS-SVM). An excellent classification with an accuracy of 92.22% in the prediction set was achieved. Using the distribution information of THz reflection signal intensity from sample surface and absorption coefficient of the sample, an attempt was made to use the TPI system to identify the boundaries of the different tissues involved (prostate tumors, normal and smooth muscles). The location of three identified regions in the terahertz images (frequency domain slice absorption coefficient imaging, 1.2 THz) were compared with those obtained from the histopathologic examination. The tissue tumor region had a distinctively visible color and could well be distinguished from other tissue regions in terahertz images. Results indicate that a THz spectroscopy imaging system can be efficiently used in conjunction with the proposed advanced computer-based mathematical analysis method to identify tumor regions in the paraffin tissue mass of prostate cancer.

Tomsk State University researchers develop new approach to diagnose adenocarcinoma

https://www.news-medical.net/news/20191113/TSU-researchers-develop-new-approach-to-diagnose-adenocarcinoma.aspx

Scientists have developed a new approach to the diagnosis of adenocarcinoma - a malignant tumor of the prostate gland. Artificial intelligence is used to determine oncopathology and determine the stage of the disease with this approach. The scientific model with 100% accuracy distinguishes between healthy tissue and pathology.

The gold standard for the diagnosis of cancer is histology, during which tissue from a patient is examined for malignant changes. So that the samples can be stored for a long time, they are dehydrated and poured with paraffin. Then experts make thin sections and examine these “slides” using a microscope.

Typically, several people work with prostate biopsy specimens, after examining the sections, they make a collegial decision. The human factor cannot be canceled, therefore, due to subjective assessment, there are erroneous conclusions. We tried to solve this problem using IT technologies - we developed a computer model and, through machine learning, taught her how to detect abnormal areas using a tool such as terahertz spectroscopy."

Yuri Kistenev, executive director of the TSU Institute of Biomedicine

According to Yuri Kistenev, in the process of analysis, artificial intelligence not only detects the presence of cancer cells but also evaluates the tumor according to the Gleason score. It is traditionally used in the diagnosis of prostate cancer to determine the degree of malignancy of the process (indicators from 1 to 10), which is important in terms of predicting the course of the disease.

"Using terahertz spectroscopy makes it possible to visualize a sample very well since a laser reads from 2500 to 4000 points in a small area," says Anastasia Knyazkova, one of the project executors. "In the process of training, a computer model was trained on samples of healthy and diseased tissue. Thus, AI learned to separate the norm and pathology. A test of his ability to verify adenocarcinoma was carried out on that part of the samples that were not used in training. Assessment of the malignancy of the process was carried out for samples having a rating of 4 and 8 on the Gleason scale. The accuracy of the differential diagnosis was 100 percent."

OT- SpectroscopyNOW- Reflecting on the prostate: Cancer margins

http://www.spectroscopynow.com/uv/details/highlight/14de3346bad/Last-Months-Most-Accessed-Feature-Reflecting-on-the-prostate-Cancer-margins.html

Reflecting on prostate cancer

Light reflectance spectroscopy, which measure backscattered light, can be used to differentiate between malignant and benign prostatic tissue with 85 percent accuracy, according to US researchers. The finding might be used in real-time tissue analysis to guide the surgeon during prostate cancer.

Urologist Jeffrey Cadeddu of the University of Texas Southwestern Medical Center and colleagues, Aaron Lay, Payal Kapur and Claus Roehrborn, explain that the surgical removal of all cancerous tissue and the sparing of the surrounding healthy tissue during a prostatectomy is not always a completely successful procedure. The removal of healthy tissue can cause problems but leaving behind malignant cells can lead to the recurrence of the prostate cancer.

A real-time approach to guide the surgeon's knife more precisely than ever before could improve the outcome for thousands of men undergoing treatment each year. Radical surgery to remove the prostate gland is often called for, but traditional techniques to assess how much surrounding tissue needs to be removed concurrently are time consuming and do not have proven clinical utility, although cutting too shallow can leave behind problematic cells, "positive surgical margins."

Minimal invasion

"We used a novel light reflectance spectroscopy probe to evaluate surgical margins on radical prostatectomy tissue specimens and correlated the findings with pathological examination," explains Cadeddu; he and his team published details in The Journal of Urology recently. Follow-up studies will be needed to confirm how well the spectroscopic technique might work in the operating theatre.

The team enrolled men with intermediate to high-risk disease requiring radical prostatectomy and examined the prostate tissue following removal with spectroscopic technique, focusing specifically on suspicious malignant and benign prostate margins. Each sample was analyzed and correlated with pathological samples, which were analysed after surgery. In total light reflectance spectroscopy was carried out on 17 prostate gland specimens of which 11 were proven positive histologically; 22 negative surgical margins were measured. The team found that their optical probe could predict positive surgical margins with 85 percent sensitivity, 86 percent specificity, and 86 percent accuracy.

Survival improver?

"This study highlights one of a growing number of technology platforms that aim to improve the outcomes of cancer surgery," explains Cadeddu, an expert in minimally invasive urological surgical techniques. "Further study is required to determine whether such analysis may be used in real time to improve surgical decision-making and decrease the amount of tissue surgeons need to remove."

Prostate cancer is the most common form of the disease in men, second only to skin cancer. It is the second biggest cancer killer in men after lung cancer with more than one in ten affected dying of the disease. "Our next step is to expand to more patients and develop a larger experience to further improve accuracy of technology," Cadeddu told SpectroscopyNOW. "Ultimately, we hope to see the technology commercialize