Secondary cytoreductive surgery in recurrent uterine leiomyosarcoma: a multi-institutional study

Prognostic factors of patients with recurrent uterine malignancies undergoing secondary cytoreductive surgery

BACKGROUND

Several studies have demonstrated that secondary cytoreductive surgery (SCS) for patients with recurrent uterine malignancies may improve the survival. However, the selection criteria for SCS remain to be defined. This study aimed to assess the outcome of SCS and to explore factors that may influence the prognosis.

METHODS

Data of patients with recurrent uterine malignancies who received SCS in our hospital between January 2005 and January 2015 were retrospectively analyzed. Patients were assigned into endometrial carcinoma (EC) group and uterine sarcoma (US) group.

RESULTS

84 cases in total were involved in the study, including 47 cases with recurrent EC and 37 cases with recurrent US. The 5-year survival of cases with recurrent EC and recurrent US was 59.6% and 33.3%, respectively. Recurrent EC cases with a lower tumor grade (G1/G1-G2/G2), size of the largest tumor ≤ 6 cm, single recurrent tumor, a history of adjuvant therapy, as well as recurrent US cases with younger age, a longer disease-free interval (DFI) before SCS (≥ 12 months), no peritoneal dissemination, and a history of complete cytoreduction were associated with a longer survival. The number of recurrent tumors was found as an independent prognostic factor of SCS.

CONCLUSION

Recurrent EC cases with a lower tumor grade, smaller tumor size, single tumor, a history of adjuvant therapy, as well as recurrent US cases with younger age, a longer DFI before SCS, no peritoneal dissemination, and a history of complete cytoreduction were more likely to benefit from SCS.

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases

Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists' diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.

Radiomics and artificial intelligence in malignant uterine body cancers: Protocol for a systematic review

INTRODUCTION

Uterine body cancers (UBC) are represented by endometrial carcinoma (EC) and uterine sarcoma (USa). The clinical management of both is hindered by the complex classification of patients into risk classes. This problem could be simplified through the development of predictive models aimed at treatment tailoring based on tumor and patient characteristics. In this context, radiomics represents a method of extracting quantitative data from images in order to non-invasively acquire tumor biological and genetic information and to predict response to treatments and prognosis. Furthermore, artificial intelligence (AI) methods are an emerging field of translational research, with the aim of managing the amount of data provided by the various -omics, including radiomics, through the process of machine learning, in order to promote precision medicine.

OBJECTIVE

The aim of this protocol for systematic review is to provide an overview of radiomics and AI studies on UBCs.

METHODS AND ANALYSIS

A systematic review will be conducted using PubMed, Scopus, and the Cochrane Library to collect papers analyzing the impact of radiomics and AI on UBCs diagnosis, prognostic classification, and clinical outcomes. The PICO strategy will be used to formulate the research questions: What is the impact of radiomics and AI on UBCs on diagnosis, prognosis, and clinical results? How could radiomics or AI improve the differential diagnosis between sarcoma and fibroids? Does Radiomics or AI have a predictive role on UBCs response to treatments? Three authors will independently screen articles at title and abstract level based on the eligibility criteria. The risk of bias and quality of the cohort studies, case series, and case reports will be based on the QUADAS 2 quality assessment tools.

TRIAL REGISTRATION

PROSPERO registration number: CRD42021253535.

Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review

Background: Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs). Methods: Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535). Results: The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups. Conclusion: To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.

Identification of Somatic Genetic Alterations Using Whole-Exome Sequencing of Uterine Leiomyosarcoma Tumors

Background

The genomic abnormalities associated with uterine leiomyosarcoma (uLMS) have not been fully elucidated to date.

Objective

To understand the pathogenesis of uLMS and to identify driver mutations and potential therapeutic targets in uLMS.

Methods

Three matched tumor-constitutional DNA pairs from patients with recurrent uLMS were subjected to whole-exome capture and next-generation sequencing. The role of the selected gene SHARPIN in uLMS was analyzed by the CCK-8 assay and colony formation assay after specific siRNA knockdown.

Results

We identified four genes with somatic SNVs, namely, SLC39A7, GPR19, ZNF717, and TP53, that could be driver mutations. We observed that 30.7% (4/13) of patients with uLMS had TP53 mutations as analyzed by direct sequencing. Analysis of somatic copy number variants (CNVs) showed regions of chromosomal gain at 1q21-23, 19p13, 17q21, and 17q25, whereas regions of chromosomal loss were observed at 2q35, 2q37, 1p36, 10q26, 6p22, 8q24, 11p15, 11q12, and 9p21. The SHARPIN gene was amplified in two patients and mutated in another (SHARPIN: NM_030974: exon2: c.G264C, p.E88D). Amplification of the SHARPIN gene was associated with shorter PFS and OS in soft tissue sarcoma, as shown by TCGA database analysis. Knockdown of SHARPIN expression was observed to decrease cell growth and colony formation in uterine sarcoma cell lines.

Conclusions

Exome sequencing revealed mutational heterogeneity of uLMS. The SHARPIN gene was amplified in uLMS and could be a candidate oncogene.