Sakhr Alshwayyat

Personalized approach to malignant struma ovarii: Insights from a web‐based machine learning tool

AbstractObjectivesMalignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO.MethodsIn this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5‐year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan–Meier survival analysis.ResultsThe study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K‐nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients.ConclusionThe present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision‐making processes.

Survival rates and predictors in gestational choriocarcinoma: Is chemotherapy always the answer?

Gestational choriocarcinoma is a highly malignant form of gestational trophoblastic neoplasia characterized by early vascular invasion and a strong tendency for widespread metastasis. To date, there is no consensus in the FIGO recommendations regarding when chemotherapy should be initiated following diagnosis. This study aimed to evaluate the impact of chemotherapy on survival and develop machine learning (ML) prognostic models for patients with gestational choriocarcinoma. We analyzed data from the SEER database [2000–2020]. Patients with histologically confirmed GC arising from the placenta were included, while those with other malignancies or missing key data were excluded. We conducted a Cox regression analysis for prognostic factors and developed ML models (using 5 algorithms) to predict 5-year survival rates. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan–Meier survival analysis. This study included 732 patients with a median age of 32 years (54.5% ≥30 years); most were White (66.4%), and 44.3% had metastatic disease at diagnosis. Of these, 283 received chemotherapy, 116 underwent surgery alone, and 333 underwent both surgery and chemotherapy. Survival analysis showed no significant differences in survival between the treatment modalities. Multivariate Cox regression analysis identified older age, metastasis, and marital status as significant prognostic factors. Among the ML models, Random Forest Classifier achieved the highest performance. Feature importance analysis identified age, marital status, and metastasis as the most influential survival factors. The study suggests that chemotherapy may not have benefit for survival. Further multicenter prospective studies are needed to evaluate the importance of chemotherapy initiation.

Outcomes in gestational and non-gestational choriocarcinoma: A retrospective cohort study with nomograms and web tools

Background: Choriocarcinoma (CC), a rare and aggressive form of cancer, is composed of cytotrophoblasts and syncytiotrophoblasts. It is present in two subtypes: gestational choriocarcinoma (GCC) and non-gestational choriocarcinoma (NGCC). Recognizing the disparities between GCC and NGCC is essential for the precise staging, prognosis, and determination of the primary treatment strategy. Objective: This study aimed to differentiate clinical outcomes, treatment responses, and prognostic factors between GCC and NGCC and to introduce innovative tools for personalized treatment strategies. Design: A retrospective cohort study with Survival Analysis and Nomogram Development. Methods: We analyzed data from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database and identified female patients diagnosed with GCC and NGCC between 2000 and 2020. The clinicopathological features of each group were compared using the chi-square test. Kaplan-Meier curves, log-rank tests, and Cox proportional hazard regression were used to assess overall survival and cancer-specific survival and to determine risk factors. The 5-year survival predicting nomogram was constructed, evaluated, and validated. Results: The study included 919 patients with 719 CC and 200 patients with NGCC. The NGCC group was characterized by older age, a higher proportion of married individuals, more advanced disease stages, larger tumor sizes, and a higher frequency of surgical interventions than the GCC group. NGCC was associated with worse survival rates than GCC patients. Conclusions: This study highlights the critical role of chemotherapy in improving the survival of patients with NGCC, in contrast to its limited effect on GCC. The negative prognosis associated with radiotherapy underscores the urgent need for further investigation to optimize its use. In addition, the introduction of the first web-based survival prediction tool and predictive nomogram marked a significant advancement in personalized treatment strategies, enabling improved clinical outcomes by tailoring therapy to individual patients.

Presentation and treatment of two cases of malignant struma ovarii

Abstract Background Malignant Struma Ovarii (MSO) is a rare type of germ cell tumour which is diagnosed postoperatively on surgical pathology specimens by the presence of differentiated thyroid cancer in mature cystic teratomas in the ovaries. Treatment and follow-up procedures are not clearly established due to the paucity of MSO cases. Case 1 A 44-year-old multiparous female presented with an irregular period. Ultrasound showed a left ovarian lesion mostly a dermoid cyst, however, CT showed a 3.8 × 2.7 × 4 cm complex cystic lesion with thick septation and enhancing soft tissue component. Laparoscopic left salpingo-oophorectomy was performed and histopathology showed a follicular variant of papillary thyroid carcinoma arising in a mature cystic teratoma. Peritoneal cytology was positive for malignancy. A thyroid function test was normal before surgery. Total thyroidectomy was performed followed by radioactive (RAI) iodine therapy. Later, a total laparoscopic hysterectomy and right salpingo-oophorectomy were performed. There is no evidence of recurrent disease during the 26-months follow-up. Case 2 A 46-year-old single female presented with left lower abdominal pain that had persisted for 2 months. Imaging revealed an 8 × 9 × 9.5 cm left ovarian mass. Laparoscopic left salpingo-oophorectomy was performed and histopathology showed mature cystic teratoma with small papillary thyroid cancer. CT showed no evidence of metastatic disease. Later, the patient had a total thyroidectomy followed by radioactive (RAI) iodine therapy. She was started on thyroxine and later had total abdominal hysterectomy and right salpingo-oophorectomy. Conclusion MSO is a very rare tumour. Preoperative diagnosis is very difficult because of the nonspecific symptoms and the lack of specific features in imaging studies. Also, there is no consensus on the optimal treatment of women with MSO. Our two cases add to the limited number of MSO cases.