Considerations on chemical composition of psammoma bodies: Automated detection strategy by infrared microspectroscopy in ovarian and thyroid cancer tissues

The clinicopathologic significance of psammoma bodies in cytology specimens: A series of 78 cases

BACKGROUND

Currently the clinicopathologic significance of psammoma bodies in cytology specimens are not completely understood, including types of cytology specimens and pathologic conditions frequently associated with this unique cytologic feature. In this study, we undertook a retrospective approach to review the specimen types, cytology preparations, patient characteristics, organs or tissues involved and differential diagnoses in cytology specimens with the finding of psammoma bodies.

METHODS

Cytology cases with the finding of psammoma bodies from January 2004 to December 2022 were retrieved from our institution's pathology databases, and their clinicopathological features were reviewed.

RESULTS

A total of 78 cytology specimens with the finding of psammoma bodies were recorded in our CoPath system. The mean age at diagnosis was 59 years. The patient group showed female gender predominancy (90%). FNA specimens comprised about 38.5% of total cases. Other common specimen types were body cavity fluids (38.5%), including pleural effusion and peritoneal fluid, and about 20.5% of the cases were pelvic washing performed during gynecologic surgeries. Most cytology cases with psammoma bodies had a malignant diagnosis (69%). About 18% of the cases were in the indeterminate diagnostic categories, with 12% suspicious for malignancy and 6% of the cases with atypical cells. About 5% of cases were placed in the neoplastic category, while 8% of cases were negative for malignancy. About 79% of peritoneal cytology with psammoma bodies were neoplastic and mostly gynecologic tumors. Pleural fluids with psammoma bodies were very likely to be malignant and involved by serous carcinoma (15 of 16 cases, 94%). Papillary thyroid carcinoma was the second most common malignancy in our series, present in about 53% of thyroid cytologies with the finding of psammoma bodies.

CONCLUSION

Our study showed that psammoma bodies in cytology preparations were more often associated with malignancies in our study of 78 cytology specimens (69%). The most sampled location in our study was peritoneal cavity, followed by pleural cavity, thyroid, lymph nodes, neck masses, and omentum. The clinicopathologic value of psammoma bodies in predicting malignancy varies depending on locations and specimen types.

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms

Introduce

Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis.

Methods

This study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm.

Result

The range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87.

Conclusion

The results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.