[Adrenocortical cancer: morphological variants, immunohistochemical characteristics]

[The algorithm for morphological assessment of malignant potential of adrenocortical tumors using mathematical modeling method]

OBJECTIVE

To develop the mathematical model with high sensitivity and specificity to assess the malignant potential of adrenal cortical tumors, which can be used to diagnose adrenocortical carcinoma (ACC) in adults.

MATERIAL AND METHODS

Pathomorphological examination of surgical and consultative material of adrenocortical neoplasms was carried out. All cases were verified according to the WHO Classification of adrenal gland tumors (5th ed., 2022), the tumor's histogenesis was confirmed by immunohistochemical examination. Statistical analysis of the histological and immunohistochemical factors in terms of their value in relation to the diagnosis of ACC was carried out on Python 3.1 in the Google Colab environment. ROC analysis was used to identify critical values of predictors. The cut-off point was selected according to the Youden`s index. Logistic regression analysis using l1-regularisation was performed. To validate the model, the initial sample was divided into training and test groups in the ratio of 9:1, respectively.

RESULTS

The study included 143 patients divided into training (128 patients) and test (15 patients) samples. A prognostic algorithm was developed, which represent a diagnostically significant set of indicators of the currently used Weiss scale. The diagnosis is carried out in 3 stages. This mathematical model showed 100% accuracy (95% CI: 96-100%) on the training and test samples.

CONCLUSION

The developed algorithm could solve the problem of subjectivity and complexity in the interpretation of some of the criteria of current diagnostic algorithms. The new model is unique in that, unlike others, it allows verification of all morphological variants of ACC.

[Metastatic lesions of the adrenal glands. The experience of Endocrinology Research Center.]

Differentiation between benign, primary and secondary malignant tumors is a critical problem in the clinical treatment of adrenal tumors, especially in patients with isolated adrenal lesions. In most cases, the correct diagnosis can be established microscopically with standard staining with hematoxylin and eosin. However, there are cases when it is almost impossible to distinguish metastasis from primary adrenal cancer, so an accurate diagnosis requires an immunohistochemical examination.This article presents five unique observations of secondary adrenal tumors that were diagnosed by us in the current surgical material: metastasis of clear cell renal cell carcinoma, follicular variant of papillary thyroid cancer, metastasis of keratinizing squamous cell carcinoma of the cervix, lymphoepithelioma-like carcinoma of the bladder, as well as malignant mesothelioma. Taking into account the extreme rarity of the presented observations, we present an analysis of the literature data.

The new histological system for the diagnosis of adrenocortical cancer

Introduction

Adrenocortical cancer (ACC) is a rare malignant tumor that originates in the adrenal cortex. Despite extensive molecular-genetic, pathomorphological, and clinical research, assessing the malignant potential of adrenal neoplasms in clinical practice remains a daunting task in histological diagnosis. Although the Weiss score is the most prevalent method for diagnosing ACC, its limitations necessitate additional algorithms for specific histological variants. Unequal diagnostic value, subjectivity in evaluation, and interpretation challenges contribute to a gray zone where the reliable assessment of a tumor's malignant potential is unattainable. In this study, we introduce a universal mathematical model for the differential diagnosis of all morphological types of ACC in adults.

Methods

This model was developed by analyzing a retrospective sample of data from 143 patients who underwent histological and immunohistochemical examinations of surgically removed adrenal neoplasms. Statistical analysis was carried out on Python 3.1 in the Google Colab environment. The cutting point was chosen according to Youden's index. Scikit-learn 1.0.2 was used for building the multidimensional model for Python. Logistical regression analysis was executed with L1-regularization, which is an effective method for extracting the most significant features of the model.

Results

The new system we have developed is a diagnostically meaningful set of indicators that takes into account a smaller number of criteria from the currently used Weiss scale. To validate the obtained model, we divided the initial sample set into training and test sets in a 9:1 ratio, respectively. The diagnostic algorithm is highly accurate [overall accuracy 100% (95% CI: 96%-100%)].

Discussion

Our method involves determining eight diagnostically significant indicators that enable the calculation of ACC development probability using specified formulas. This approach may potentially enhance diagnostic precision and facilitate improved clinical outcomes in ACC management.

Metastatic Adrenocortical Carcinoma With Chromothripsis: A Case Report and Literature Review

Metastatic adrenocortical carcinoma (ACC) often has a poor outcome, with a five-year survival of less than 25%. We report a rare case of metastatic ACC with a myxoid variant with chromothripsis. We review the histologic variants of ACC, including myxoid type, molecular drivers, and current and investigational therapies for adrenocortical carcinoma. We also discuss the mechanism of chromothripsis, chromothripsis in ACC tumorigenesis, and propose potential therapies targeting chromothripsis.

[Multifactorial assessment of prognostic features of adrenocortical cancer]

OBJECTIVE

To determine the predictors of the aggressive course of adrenocortical cancer (ACC). ACC is a rare malignant neoplasm of the adrenal cortex with a variable prognosis. At present, predicting the course of the disease and clinical outcome in patients presents significant difficulties, since many aspects of the treatment of ACC have not been studied or require clarification. In particular, the prognostic value of markers that are used in clinical practice has not been fully determined.

MATERIAL AND METHODS

Histological and immunohistochemical studies of the surgical material of adrenal neoplasms were performed. The study included 73 patients with a histologically confirmed diagnosis of ACC: 51 (69.9%) women and 22 (30.1%) men aged 17 to 82 years. Cox regression model was used for survival analysis. Statistically significant factors (p<0.05) according to the results of independent regression analysis were included in the Kaplan-Meier analysis followed by pairwise comparison using the log-rank test.

RESULTS

An increased risk of recurrence is associated with the presence of pathological mitoses and excessive secretion of aldosterone, death - with stage IV according to the classification of the European Network for the Study of Adrenal Tumors (ENSAT), relapse and death - the value of mitotic activity more than 20, the Ki-67 index more than 12, the classic variant. Mitotic activity and hormonal activity were independent predictors of recurrence-free survival, mitotic activity and ENSAT stage were independent predictors of overall survival.

CONCLUSIONS

In the present work, potential predictors of the course of ACC are identified, which are available for use in routine practice. Based on the totality of clinical, morphological, immunohistochemical data used in the diagnosis of ACC, a conception of the patient's survival can be obtained.