The prognosis of patients with small cell carcinoma of the cervix: a retrospective study of the SEER database and a Chinese multicentre registry

Assessing treatment effects with adjusted restricted mean time lost in observational competing risks data

BACKGROUND

According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard ratio (SHR) are relative indicators and may present challenges in terms of proportional hazards assumption and clinical interpretation. Recently, the restricted mean time lost (RMTL) has been recommended as a supplementary measure for better clinical interpretation. Moreover, for observational study data in epidemiological and clinical settings, due to the influence of confounding factors, covariate adjustment is crucial for determining the causal effect of treatment.

METHODS

We construct an RMTL estimator after adjusting for covariates based on the inverse probability weighting method, and derive the variance to construct interval estimates based on the large sample properties. We use simulation studies to study the statistical performance of this estimator in various scenarios. In addition, we further consider the changes in treatment effects over time, constructing a dynamic RMTL difference curve and corresponding confidence bands for the curve.

RESULTS

The simulation results demonstrate that the adjusted RMTL estimator exhibits smaller biases compared with unadjusted RMTL and provides robust interval estimates in all scenarios. This method was applied to a real-world cervical cancer patient data, revealing improvements in the prognosis of patients with small cell carcinoma of the cervix. The results showed that the protective effect of surgery was significant only in the first 20 months, but the long-term effect was not obvious. Radiotherapy significantly improved patient outcomes during the follow-up period from 17 to 57 months, while radiotherapy combined with chemotherapy significantly improved patient outcomes throughout the entire period.

CONCLUSIONS

We propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data.

Causes of death analysis and the prognostic model construction in neuroendocrine carcinoma of the cervix: A SEER-based study

PURPOSE

Neuroendocrine carcinoma of the cervix (NECC) is rare but results in poor prognosis. The causes of death (CODs) in NECC patients are rarely reported. Our study aimed to explore the distributions of death causes of NECC patients compared with squamous cell carcinoma (SCC) and adenocarcinoma (ADC) and to develop a validated survival prediction model.

METHODS

Patients diagnosed with NECC, SCC, or ADC were identified from the Surveillance, Epidemiology, and End Results Program database from 1975 to 2019. We analyzed the standardized mortality ratio (SMR) to determine each cause of death for each survival time category. The Kaplan-Meier method was used for survival analysis. Univariate and multivariate Cox regression analyses were used to establish a nomogram model.

RESULTS

A total of 358 NECC patients were included in this study, and 270 (75.4%) died during the follow-up period. Patients with NECC had 5.55 times (95% CI, 4.53-6.79, p < 0.0001) higher risk of death compared with patients with SCC and 10.38 times (95% CI, 8.28-13.01, p < 0.0001) higher compared with ADC. Cervical cancer is the main cause of death in NECC. As the diagnosis time increased, the risk of death from all causes and cervix cancer gradually decreased. While after at least 10 years of follow-up time, the highest and most dramatical SMR values were observed for metastasis (SMR, 138.81; 95% CI, 37.82-355.40; p < 0.05) and other cancers as the reason for death has an over 7-fold higher SMR (SMR: 7.07; 95% CI: 2.60-15.40, p < 0.05) more than 5 years after the cancer diagnosis. Race, FIGO stage, and surgery were independent risk factors for the overall survival (OS) of NECC patients. For the predictive nomogram, the C-index was 0.711 (95% CI: 0.697-0.725) and was corrected to 0.709 (95% CI: 0.680, 0.737) by bootstrap 1000 resampling validation.

CONCLUSION

Compared with SCC and ADC, NECC patients have an elevated risk of mortality due to cervical cancer and metastasis. We successfully constructed a prognostic nomogram for patients with NECC. Based on refractoriness and high mortality of NECC, targeted treatment strategies and follow-up plans should be further developed according to the risk of death and distribution characteristics of CODs.

Liquid-Based Cytology of Small Cell Carcinoma of the Cervix: A Multicenter Retrospective Study

Background/Aims

There are currently few reports describing the liquid-based cytological characteristics of small cell neuroendocrine carcinoma of the cervix. This study aimed to retrospectively analyze these features to reduce missed or misdiagnosis.

Methods

A total of 11 patients with histologically diagnosed small cell carcinoma of the cervix from three hospitals between 2017 and 2023 were included in this study. The cytological morphology of small cell carcinoma of the cervix and causes of missed or misdiagnosis were analyzed and summarized through a review of clinical data, liquid-based cytology, histology, immunohistochemistry, and human papillomaviruses (HPV) test results.

Results

In this study, the positivity rate of preliminary cytological screening was 63.6% (7/11); however, no cases were accurately diagnosed as small cell carcinoma of the cervix. A total of 36.4% (4/11) of small cell carcinoma of the cervix cases were cytologically negative; retrospective cytology found that two of these were false negatives. The main cytological features of small cell carcinoma of the cervix were summarized. Most of the liquid-based cytology smear cells were dense, and almost all cases showed clustered and scattered cytoplasm-scanty tumor cells. The tumor cells were all deeply stained and relatively consistent small cells. Most cases showed typical nuclear molding, chromatin stippling, and no obvious nucleoli. Mild nuclear smears, nuclear fragments, and mitotic figures were seen in most cases.

Conclusion

Liquid-based cytology has a high rate of missed diagnosis and misdiagnosis in small cell carcinoma of the cervix. This study confirms that reviewing cytology results can effectively reduce this proportion and that increasing understanding of small cell carcinoma of the cervix morphology is conducive to improving the cytology-based diagnosis rate.

Pan-Canadian Analysis of Practice Patterns in Small Cell Carcinoma of the Cervix: Insights from a Multidisciplinary Survey

Small-cell neuroendocrine carcinoma of the cervix (SCNECC) is a rare cancer with poor prognosis, with limited data to guide its treatment. The objective of this study was to evaluate practice patterns in the management of SCNECC. A 23-question online survey on management of SCNECC was disseminated to Canadian gynecologic oncologists (GO), radiation oncologists (RO) and medical oncologists (MO). In total, 34 practitioners from eight provinces responded, including 17 GO, 13 RO and four MO. During staging and diagnosis, 74% of respondents used a trimodality imaging approach, and 85% tested for neuroendocrine markers. In early-stage (1A1-1B2) SCNECC, 87% of practitioners used a surgical-based approach with various adjuvant and neoadjuvant treatments. In locally advanced (1B3-IVA) SCNECC, 53% favored primary chemoradiation, with cisplatin and etoposide, with the remainder using surgical or radiation-based approaches. In metastatic and recurrent SCNECC, the most common first-line regimen was etoposide and platinum, and 63% of practitioners considered clinical trials in the first line setting or beyond. This survey highlights diverse practice patterns in the treatment of SCNECC. Interdisciplinary input is crucial to individualizing multimodality treatment, and there is a need for prospective trials and intergroup collaboration to define the optimal approach towards managing this rare cancer type.

Advancements in the Understanding of Small-Cell Neuroendocrine Cervical Cancer: Where We Stand and What Lies Ahead

Small-cell neuroendocrine cervical carcinoma (SCNCC) is a rare yet aggressive gynecological malignancy associated with dismal clinical outcomes. Its rarity has led to a limited number of retrospective studies and an absence of prospective research, posing significant challenges for evidence-based treatment approaches. As a result, most gynecologic oncology centers have limited experience with this tumor, emphasizing the urgent need for a comprehensive review and summary. This article systematically reviews the pathogenesis, immunohistochemical and molecular characteristics, prognostic factors, and clinical management of gynecologic SCNCC. We specifically focused on reviewing the distinct genomic characteristics of SCNCC identified via next-generation sequencing technologies, including loss of heterozygosity (LOH), somatic mutations, structural variations (SVs), and microRNA alterations. The identification of these actionable genomic events offers promise for discovering new molecular targets for drug development and enhancing therapeutic outcomes. Additionally, we delve deeper into key clinical challenges, such as determining the optimal treatment modality between chemoradiation and surgery for International Federation of Gynecology and Obstetrics (FIGO) stage I phase patients within a precision stratification framework, as well as the role of targeted therapy within the homologous recombination (HR) pathway, immune checkpoint inhibitors (ICIs), and prophylactic cranial irradiation (PCI) in the management of SCNCC. Finally, we anticipate the utilization of multiple SCNCC models, including cancer tissue-originated spheroid (CTOS) lines and patient-derived xenografts (PDXs), to decipher driver events and develop individualized therapeutic strategies for clinical application.

Survival analysis and clinicopathological features of patients with stage IA lung adenocarcinoma

Background

With the development of medical technology and change of life habits, early-stage lung adenocarcinoma (LUAD) has become more common. This study aimed to systematically analyzed clinicopathological factors associated to the overall survival (OS) of patients with Stage IA LUAD.

Methods

A total of 5942 Stage IA LUAD patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier methods and log-rank tests were used to compare the differences in OS. A nomogram constructed based on the Cox regression was evaluated by Concordance index (C index), calibration curve, decision curve analysis (DCA) and area under curve (AUC). And 136 patients were recruited from Shandong Province Hospital for external validation.

Results

Cox analysis regression indicated that 12 factors, such as Diagnosis to Treatment Interval (DTI) and Income Level, were independent prognostic factors and were included to establish the nomogram. The C-index of our novel model was 0.702, 0.724 and 0.872 in the training, internal and external validation cohorts, respectively. The 3-year and 5-year survival AUCs and calibration curves showed excellent agreement in each cohort. Some new factors in the SEER database, including DTI and Income Level, were firstly confirmed as independent prognostic factors of Stage IA LUAD patients. The distribution of these factors in the T1a, T1b, and T1c subgroups differed and had different effects on survival.

Conclusion

We summarized 12 factors that affect prognosis and constructed a nomogram to predict OS of Stage IA LUAD patients who underwent operation. For the first time, new SEER database parameters, including DTI and Income Level, were proved to be survival-related.

Prognostic model for survival in patients with neuroendocrine carcinomas of the cervix: SEER database analysis and a single-center retrospective study

OBJECTIVE

Neuroendocrine carcinoma of the cervix (NECC) is extremely rare in clinical practice. This study aimed to methodologically analyze the clinicopathological factors associated with NECC patients and to develop a validated survival prediction model.

METHODS

A total of 535 patients diagnosed with NECC between 2004 and 2016 were identified from the Surveillance, Epidemiology and End Results (SEER) database, while 122 patients diagnosed with NECC at Yunnan Cancer Hospital (YCH) from 2006 to 2019 were also recruited. Patients from the SEER database were divided into a training cohort (n = 376) and a validation cohort (n = 159) in a 7:3 ratio for the construction and internal validation of the nomogram. External validation was performed in a cohort at YCH. The Kaplan-Meier method was used for survival analysis, the Log-rank method test was used for univariate analysis of prognostic influences, and the Cox regression model was used for multivariate analysis.

RESULTS

The 3-year and 5-year overall survival (OS) rates for patients with NECC in SEER were 43.6% and 39.7%, respectively. In the training cohort, multivariate analysis showed independent prognostic factors for NECC patients including race, tumor size, distant metastasis, stage, and chemotherapy (p<0.05). For extended application in other cohorts, a nomogram including four factors without race was subsequently created. The consistency index (C-index) of the nomogram predicting survival was 0.736, which was well-validated in the validation cohorts (0.746 for the internal validation cohort and 0.765 for the external validation cohort). In both the training and validation cohorts, the 3-year survival rates predicted by the nomogram were comparable to the actual ones. We then succeeded in dividing patients with NECC into high- and low-risk groups concerning OS using the nomogram we developed. Besides, univariate analysis showed that chemotherapy ≥4 cycles may improve the OS of patients at YCH with NECC.

CONCLUSION

We successfully constructed a nomogram that precisely predicts the OS for patients with NECC based on the SEER database and a large single-center retrospective cohort. The visualized and practical model can distinguish high-risk patients for recurrence and death who may benefit from clinical trials of boost therapy effectively. We also found that patients who received more than 4 cycles of chemotherapy acquired survival benefits than those who received less than 4 cycles.

A Competing Risk Nomogram for Prediction of Prognosis in Patients With Primary Squamous Cell Thyroid Carcinoma

Objective: Primary squamous cell thyroid carcinoma (PSCTC) is an extremely rare carcinoma, accounting for less than 1% of all thyroid carcinomas. However, the factors contributing to PSCTC outcomes remain unclear. This study aimed to identify the prognostic factors and develop a prognostic predictive model for patients with PSCTC. Methods: The analysis included patients diagnosed with thyroid carcinoma between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Prognostic differences among the 5 pathological types of thyroid carcinomas were analyzed. To determine prognostic factors in PSCTC patients, the Cox regression model and Fine-Gray competing risk model were utilized. Based on the Fine-Gray competing risk model, a nomogram was established for predicting the prognosis of patients with PSCTC. Results: A total of 198,757 thyroid carcinoma patients, including 218 PSCTC patients, were identified. We found that PSCTC and anaplastic thyroid cancer had the worst prognosis among the 5 pathological types of thyroid carcinoma (P < .001). According to univariate and multivariate Cox regression analyses, age (71-95 years) was an independent risk factor for poorer overall survival and disease-specific survival in PSCTC patients. Using Fine-Gray regression analysis, the total number of in situ/malignant tumors for patient (Number 1) (≥2) was identified as an independent protective factor for prognosis of PSCTC. The area under the curve, the concordance index (C-index), calibration curves and decision curve analysis revealed that the nomogram was capable of predicting the prognosis of PSCTC patients accurately. Conclusion: The competing risk nomogram is highly accurate in predicting prognosis for patients with PSCTC, which may help clinicians to optimize individualized treatment decisions.

Extrapulmonary Neuroendocrine Carcinomas: Current Management and Future Perspectives

Neuroendocrine carcinomas (NECs) are poorly differentiated and highly aggressive epithelial neuroendocrine neoplasms. The most common primary site is the lung, but they may arise in every organ. Approximately 37% of extrapulmonary NECs (EP-NECs) occur in the gastroenteropancreatic (GEP) tract, followed by the genitourinary (GU) system and gynecological tract. As a result of their rarity, there is scant evidence to guide treatment recommendations, and a multidisciplinary approach is essential for the management of such patients. Platinum-based chemotherapy currently represents the standard of care for EP-NECs of any site, mirroring the management of small-cell lung cancer (SCLC), but further approaches are still under investigation. Indeed, ongoing trials evaluating targeted therapies, immune checkpoint inhibitors (ICIs), and radionuclide therapy could provide potentially breakthrough therapeutic options. Given the relative dearth of evidence-based literature on these orphan diseases, the aim of this review is to provide an overview of the pathology and current treatment options, as well as to shed light on the most pressing unmet needs in the field.

CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy

Background and purpose

To establish and validate a hybrid radiomics model to predict overall survival in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT).

Methods

We retrospectively collected 367 cervical cancer patients receiving chemoradiotherapy from the First Affiliated Hospital of Soochow University in China and divided them into a training set and a test set in a ratio of 7:3. Handcrafted and deep learning (DL)-based radiomics features were extracted from the contrast-enhanced computed tomography (CT), and the two types of radiomics signatures were calculated based on the features selected using the least absolute shrinkage and selection operator (LASSO) Cox regression. A hybrid radiomics nomogram was constructed by integrating independent clinical risk factors, handcrafted radiomics signature, and DL-based radiomics signature in the training set and was validated in the test set.

Results

The hybrid radiomics nomogram exhibited favorable performance in predicting overall survival, with areas under the receiver operating characteristic curve (AUCs) for 1, 3, and 5 years in the training set of 0.833, 0.777, and 0.871, respectively, and in the test set of 0.811, 0.713, and 0.730, respectively. Furthermore, the hybrid radiomics nomogram outperformed the single clinical model, handcrafted radiomics signature, and DL-based radiomics signature in both the training (C-index: 0.793) and test sets (C-index: 0.721). The calibration curves and decision curve analysis (DCA) indicated that our hybrid nomogram had good calibration and clinical benefits. Finally, our hybrid nomogram demonstrated value in stratifying patients into high- and low-risk groups (cutoff value: 5.6).

Conclusion

A high-performance hybrid radiomics model based on pre-radiotherapy CT was established, presenting strengths in risk stratification.