Search Results

You are looking at 1 - 2 of 2 items for

  • Author: Hélio A. Miot x
  • All content x
Clear All Modify Search
Open access

Flávia O Valentim, Bárbara P Coelho, Hélio A Miot, Caroline Y Hayashi, Danilo T A Jaune, Cristiano C Oliveira, Mariângela E A Marques, José Vicente Tagliarini, Emanuel C Castilho, Paula Soares, and Gláucia M F S Mazeto

Background

Computerized image analysis seems to represent a promising diagnostic possibility for thyroid tumors. Our aim was to evaluate the discriminatory diagnostic efficiency of computerized image analysis of cell nuclei from histological materials of follicular tumors.

Methods

We studied paraffin-embedded materials from 42 follicular adenomas (FA), 47 follicular variants of papillary carcinomas (FVPC) and 20 follicular carcinomas (FC) by the software ImageJ. Based on the nuclear morphometry and chromatin texture, the samples were classified as FA, FC or FVPC using the Classification and Regression Trees method.

Results

We observed high diagnostic sensitivity and specificity rates (FVPC: 89.4% and 100%; FC: 95.0% and 92.1%; FA: 90.5 and 95.5%, respectively). When the tumors were compared by pairs (FC vs FA, FVPC vs FA), 100% of the cases were classified correctly.

Conclusion

The computerized image analysis of nuclear features showed to be a useful diagnostic support tool for the histological differentiation between follicular adenomas, follicular variants of papillary carcinomas and follicular carcinomas.

Open access

Caroline Y. Hayashi, Danilo T. A. Jaune, Cristiano C. Oliveira, Bárbara P. Coelho, Hélio A. Miot, Mariângela E. A. Marques, José Vicente Tagliarini, Emanuel C. Castilho, Carlos Sp Soares, Flavia Rk Oliveira, Paula Soares, and Glaucia M.f.s Mazeto

Background: Thyroid nodules diagnosed as “Atypia of Undetermined Significance/Follicular Lesion of Undetermined Significance” (AUS/FLUS) or “Follicular Neoplasm/Suspected Follicular Neoplasm” (FN/SFN)”, according to Bethesda's classification, represent a challenge in clinical practice. Computerized analysis of nuclear images (CANI) could be a useful tool for these cases. Our aim was to evaluate the ability of CANI to correctly classify AUS/FLUS and FN/SFN thyroid nodules for malignancy.

Methods: We studied 101 nodules cytologically classified as AUS/FLUS (n=68) or FN/SFN (n=33) from 97 thyroidectomy patients. Slides with cytological material were submitted to manual selection and analysis of the follicular cell nuclei for morphometric and texture parameters using ImageJ software. The histologically benign and malignant lesions were compared for such parameters which were then evaluated for the capacity to predict malignancy using the Classification and Regression Trees Gini model. The Intraclass Coefficient of Correlation was used to evaluate method reproducibility.

Results: In AUS/FLUS nodule analysis, the benign and malignant nodules differed for Entropy (p<0.05), while the FN/SFN nodules differed for Fractal analysis, coefficient of variation (CV) of roughness, and CV-Entropy (p<0.05). Considering the AUS/FLUS and FN/SFN nodules separately, it correctly classified 90.0% and 100.0% malignant nodules, with a correct global classification of 94.1% and 97%, respectively. We observed that reproducibility was substantially or nearly complete (0.61-0.93) in 10 of the 12 nuclear parameters evaluated.

Conclusion: CANI demonstrated an high capacity for correctly classifying AUS/FLUS and FN/SFN thyroid nodules for malignancy. This could be a useful method to help increase diagnostic accuracy in the indeterminate thyroid cytology.