Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor

Abstract

Background: Rapid intraoperative diagnosis for unconfirmed pulmonary tumor is extremely important for determining the optimal surgical procedure (lobectomy or sublobar resection). Attempts to diagnose malignant tumors using mass spectrometry (MS) have recently been described. This study evaluated the usefulness of MS and artificial intelligence (AI) for differentiating primary lung adenocarcinoma (PLAC) and colorectal metastatic pulmonary tumor. Methods: Pulmonary samples from 40 patients who underwent pulmonary resection for PLAC (20 tumors, 20 normal lungs) or pulmonary metastases originating from colorectal metastatic pulmonary tumor (CRMPT) (20 tumors, 20 normal lungs) were collected and analyzed retrospectively by probe electrospray ionization-MS. AI using random forest (RF) algorithms was employed to evaluate the accuracy of each combination. Results: The accuracy of the machine learning algorithm applied using RF to distinguish malignant tumor (PLAC or CRMPT) from normal lung was 100%. The algorithms offered 97.2% accuracy in differentiating PLAC and CRMPT. Conclusions: MS combined with an AI system demonstrated high accuracy not only for differentiating cancer from normal tissue, but also for differentiating between PLAC and CRMPT with a short working time. This method shows potential for application as a support tool facilitating rapid intraoperative diagnosis to determine the surgical procedure for pulmonary resection.

Applications of ambient ionization mass spectrometry in 2022: An annual review

“… 56 Through the analysis of almost 300 cancerous and healthy tissue samples, the authors demonstrated the technique to have a high sensitivity and specificity, with a diagnostic accuracy of 92%. PESI‐MS was also recently applied to the analysis of pulmonary tumors 57 and liver tumours across different populations, highlighting its versatility with different tissue types. 58 Although PESI has been widely applied to the analysis of cancerous tissues, it does still require tissue biopsy, as opposed to techniques such as REIMS which can be used in situ in the operating theatre and could feasibly be used for non‐invasive analysis.…”

Diagnostic method of mass spectrometry for detecting lymph node metastasis of non‐small cell lung cancer

“…Liquid chromatography–tandem mass spectrometry (LC–MS/MS) have been developed as an innovative tool for the analysis of small samples, offering improved specificity over traditional methods. MS imaging and probe electrospray ionization (PESI)‐mass spectrometry have been described as useful tools for diagnosing lung cancer or distinguishing between normal and cancerous tissues 11,12 . PESI‐MS allows rapid evaluation and is extremely useful, but has the weakness of being able to measure only that part of the specimen obtained by puncture biopsy.…”

“…MS imaging and probe electrospray ionization (PESI)‐mass spectrometry have been described as useful tools for diagnosing lung cancer or distinguishing between normal and cancerous tissues. 11 , 12 PESI‐MS allows rapid evaluation and is extremely useful, but has the weakness of being able to measure only that part of the specimen obtained by puncture biopsy. However, diagnostic procedures to detect specific substances by LC–MS/MS in the field of lung cancer have rarely been reported.…”

Multi-classification-assisted diagnosis of multi-scale lung adenocarcinoma pathological maps based on Scalenet101

“…In medical image proce ssing, DL techniques have advantages such as high accuracy and strong adapta bility. In recent years, with the rapid development of DL technology, an increa sing number of scholars have harnessed it for medical domain, and great stride s have been made [21][22][23] . However，there are relatively few studies on the constr uction of lung adenocarcinoma data set and intelligent detection，which conflict s with the urgent treatment needs.…”