At present, the most commonly used clinical screening tool is based on prostate-specific antigen (PSA) examination. Because PSA is a tissue-specific rather than a tumor-specific marker, it has low specificity and sensitivity for prostate cancer. Although these PSA-related diagnostic models (PHI, 4Kscore) have been proved to improve the sensitivity and specificity of the early diagnosis of prostate cancer, they still do not meet the requirements of accurate diagnosis. Therefore, it is extremely important to develop a diagnosis tool with higher specificity, sensitivity and accuracy in the current prostate tumor screening strategy.

Raman spectroscopy (Raman Spectrum, RS) as a non-invasive and high specificity of material molecular detection technology, can be obtained in the molecular level, thus sensitive to detect biological samples tumor metabolism related proteins, nucleic acids, lipids and sugar composition of bio-molecules changes. As scientists pointed out in a literature in "chemical society reviews"in 2020, although SERS technology has shown good diagnostic efficacy in lots of preclinical studies in multiple tumors, it is limited to a generally small sample size and lacks external validation. There for, a clinical study of Raman spectra for tumor diagnosis is needed, which meets the following requirements: 1.An objective, fast and practical application of Raman spectral data processing is needed and deep learning method may be the best classification method; 2. It requires multicenter and large clinical samples to train deep learning diagnostic model, and verify its true efficacy through external data of prospective study.

In our preliminary study,we have collected Raman spectra data from a large cohort of 2899 patients and constructed Raman intelligent diagnostic system based on CNN model. The intelligent diagnostic system achieved accuracy of 83%. In order to obtain the highest level of clinical evidence and truly realize clinical transformation, this prospective, multi-center clinical study is designed to verify the intelligent diagnostic system for early diagnosis of prostate cancer.

Inclusion Criteria:

1. Patients with suspected prostate cancer and meet the Chinese Guidelines for Prostate Cancer (2014 edition)
2. PSA≤20;
3. The ECGO score was 0-1, and the cardiopulmonary function tolerated prostate biopsy;
4. After being fully informed of the purpose and possible risks of the study, the patient agrees to participate in the trial and signed the "Informed Consent for the use of clinical samples".

Exclusion criteria:

1. Previous history of other cancer;
2. Metabolic disorders caused by chronic renal failure or metabolic diseases, obviously abnormal blood sugar, blood lipid and plasma protein;
3. Previously taking 5- α reductase inhibitor drug;
4. History of acute prostatitis or minimally invasive surgery inside the prostate cavity for 3 months prior to puncture;
5. History of multiple blood transfusion;
6. Failure to cooperate with or refuse to participate in the clinical trial later.

Patients with suspected prostate cancer and meet the Chinese Guidelines for Prostate Cancer (2014 edition); including:

1. Digital rectal examination found prostate nodules, any PSA.
2. B ultrasound, CT, MRI found abnormal signals, any PSA.
3. PSA> 10 ng/ml, any f / t PSA and PSAD values.
4. PSA 4 ~ 10 ng/ml, abnormal f / t PSA value or abnormal PSD value.