Long term survival outcomes of stage I mucinous ovarian cancer - A clinical calculator predictive of chemotherapy benefit

Development and validation of a nomogram to predict cancer-specific survival of mucinous epithelial ovarian cancer after cytoreductive surgery

BACKGROUND

Mucinous epithelial ovarian cancer (mEOC) is a relatively uncommon subtype of ovarian cancer with special prognostic features, but there is insufficient research in this area. This study aimed to develop a nomogram for the cancer-specific survival (CSS) of mEOC based on Surveillance, Epidemiology, and End Results (SEER) database and externally validate it in National Union of Real World Gynecological Oncology Research and Patient Management (NUWA) platform from China.

METHODS

Patients screened from SEER database were allocated into training and internal validation cohort in a ratio of 7: 3, with those from NUWA platform as an external validation cohort. Significant factors selected by Cox proportional hazard regression were applied to establish a nomogram for 3-year and 5-year CSS. The performance of nomogram was assessed by concordance index, calibration curves and Kaplan-Meier (K-M) curves.

RESULTS

The training cohort (n = 572) and internal validation cohort (n = 246) were filtered out from SEER database. The external validation cohort contained 186 patients. Baseline age, tumor stage, histopathological grade, lymph node metastasis and residual disease after primary surgery were significant risk factors (p < 0.05) and were included to develop the nomogram. The C-index of nomogram in training, internal validation and external validation cohort were 0.869 (95% confidence interval [CI], 0.838-0.900), 0.839 (95% CI, 0.787-0.891) and 0.800 (95% CI, 0.738-0.862), respectively. The calibration curves of 3-year and 5-year CSS in each cohort showed favorable agreement between prediction and observation. K-M curves of different risk groups displayed great discrimination.

CONCLUSION

The discrimination and goodness of fit of the nomogram indicated its satisfactory predictive value for the CSS of mEOC in SEER database and external validation in China, which implies its potential application in different populations.

Update on Prognostic and Predictive Markers in Mucinous Ovarian Cancer

This review includes state-of-the-art prognostic and predictive factors of mucinous ovarian cancer (MOC), a rare tumor. Clinical, pathological, and molecular features and treatment options according to prognosis are comprehensively discussed. Different clinical implications of MOC are described according to the The International Federation of Gynecology and Obstetrics (FIGO) stage: early MOC (stage I-II) and advanced MOC (stage III-IV). Early MOC is characterized by a good prognosis. Surgery is the mainstay of treatment. Fertility-sparing surgery could be performed in patients who wish to become pregnant and that present low recurrence risk of disease. Adjuvant chemotherapy is not recommended, except in patients with high-risk clinical and pathological features. Regarding the histological features, an infiltrative growth pattern is the major prognostic factor of MOC. Furthermore, novel molecular biomarkers are emerging for tailored management of early-stage MOC. In contrast, advanced MOC is characterized by poor survival. Radical surgery is the cornerstone of treatment and adjuvant chemotherapy is recommended, although the efficacy is limited by the intrinsic chemoresistance of these tumors. Several molecular hallmarks of advanced MOC have been described in recent years (e.g., HER2 amplification, distinct methylation profiles, peculiar immunological microenvironment), but target therapy for these rare tumors is not available yet.

The effect of chemotherapy in patients with stage I mucinous ovarian cancer undergoing fertility-sparing surgery

Objective

To determine the effect of adjuvant chemotherapy in patients with stage I mucinous ovarian cancer (MOC) undergoing fertility-preserving surgery.

Patients and methods

The clinicopathological characteristics and survival information of young women with stage I MOC from SEER databases between 2004 and 2019 were collected. The relationship between chemotherapy and the characteristics was examined by univariate and multivariate logistic regression analyses. Univariable and multivariate Cox proportional hazards survival analysis were employed for cancer-specific survival. Cox analysis was performed to build a nomogram model.

Results

All 901 eligible patients with stage I MOC were screened from the SEER database. There were 321(35.6%) patients aged 9-30 years, 580(64.4%) aged 31-45 years, 645 (71.6%) patients with stage IA/IB, 256 (28.4%) with stage IC disease, 411(45.6%) who underwent fertility-sparing surgery, and276(30.6%) who received postoperative adjuvant chemotherapy. Multivariate logistic regression analyses showed that postoperative chemotherapy was often used in patients aged 31-45 relative to aged 9-30 (HR: 2.215, 95%CI 1.443-3.401, P &lt; 0.001) or with grade 3 compared to grade 1 tumors (HR: 7.382, 95%CI 4.054-13.443, P &lt; 0.001) or with stage IC compared to stage IA/IB (HR: 6.436, 95%CI 4.515-9.175, P &lt; 0.001) or with non-fertility sparing surgery relative to fertility-sparing (HR:2.226, 95%CI 1.490-3.327, P &lt; 0.001). Multivariate analysis for the special population with fertility preservation indicated that patients with chemotherapy (HR: 2.905, 95% CI: 0.938-6.030, P=0.068) or with grade 3 (HR: 4.750, 95% CI: 1.419-15.896, P=0.011) had a greater risk of mortality. Significant CSS differences were observed between the non-chemotherapy and chemotherapy groups in MOC when patients were stage IA/IB-grade 2 (P=0.004) (10-year CSS rates of chemotherapy=84%, non-chemotherapy = 100%), but not when they were stage IA/IB-grade 1, stage IA/IB-grade 3 or stage IC (both P&gt;0.05). A prognostic prediction nomogram model was built for stage I MOC patient who underwent fertility-sparing and the C-index was 0.709.

Discussion

The patients aged 31-45 years, with grade 3, stage IC, and non-fertility-sparing surgery were more likely to receive adjuvant chemotherapy in the real world. For stage I MOC patient who underwent fertility-sparing surgery, the choice of chemotherapy may increase the risk of death, and it should be carefully selected in clinical practice.

Clinical calculator redefines prognosis for high-risk early-stage ovarian cancers and potential to guide treatment in the adjuvant setting

OBJECTIVE

To determine the utility of a clinical calculator to redefine prognosis and need for chemotherapy among patients with early-stage high-risk epithelial ovarian cancer.

METHODS

Data were abstracted for stage I-II, high-risk ovarian cancer from the National Cancer Database from years 2005 to 2015. Based on demographic, pathologic, surgical, and laboratory characteristics, a clinical score was developed using Cox regression. Propensity score weighting was used to adjust for differences between patients who did and did not receive chemotherapy.

RESULTS

Of 8188 patients with early-stage high-risk ovarian cancer, 6915 (84%) did and 1273 (16%) did not receive chemotherapy. A clinical calculator was created utilizing age, stage, histology, grade, tumor size, number of pelvic and paraaortic lymph nodes examined, the presence of malignant ascites, and CA125. The calculator divided patients into low, moderate, and high-risk groups with 5-year OS (overall survival) of 92%, 82%, and 66%, and 10-year OS of 85%, 67%, and 44%, respectively. Chemotherapy improved 5-year OS and 10-year OS in the high-risk group (56% to 73%; p < 0.001, 34% to 48%; p < 0.001). The moderate risk group had improved 5-year OS (80% to 85%; p = 0.01) but not 10-year OS (66% to 66%; p = 0.13). Chemotherapy did not improve 5-year or 10-year OS in low-risk patients (93% to 92%, p = 1.0, 86% to 84%, p = 0.99).

CONCLUSIONS

The prognosis among high-risk early-stage ovarian cancer patients is heterogeneous. This calculator may aid in patient-centered counseling regarding potential treatment benefits.

Primary mucinous ovarian cancer: options for surgery and chemotherapy

Primary mucinous ovarian cancer is a rare type of epithelial ovarian cancer. In this comprehensive review we discuss management recommendations for the treatment of mucinous ovarian cancer. Although most tumors are stage I at diagnosis, 15-20% are advanced stage at diagnosis. Traditionally, patients with primary mucinous ovarian cancer have been treated similarly to those with the more common serous ovarian cancer. However, recent studies have shown that mucinous ovarian cancer is very different from other types of epithelial ovarian cancer. Primary mucinous ovarian cancer is less likely to spread to lymph nodes or the upper abdomen and more likely to affect younger women, who may desire fertility-sparing therapies. Surgical management of mucinous ovarian cancer mirrors surgical management of other types of epithelial ovarian cancer and includes a bilateral salpingo-oophorectomy and total hysterectomy. When staging is indicated, it should include pelvic washing, omentectomy, and peritoneal biopsies; lymph node evaluation should be considered in patients with infiltrative tumors. The appendix should be routinely evaluated intra-operatively, but an appendectomy may be omitted if the appendix appears grossly normal. Fertility preservation can be considered in patients with gross disease confined to one ovary and a normal-appearing contralateral ovary. Patients with recurrent platinum-sensitive disease whose disease distribution suggests a high likelihood of complete gross resection may be candidates for secondary debulking. Primary mucinous ovarian cancer seems to be resistant to standard platinum-and-taxane regimens used frequently for other types of ovarian cancer. Gastrointestinal cancer regimens are another option; these include 5-fluorouracil and oxaliplatin, or capecitabine and oxaliplatin. Data on heated intra-peritoneal chemotherapy (HIPEC) for mucinous ovarian cancer are scarce, but HIPEC may be worth considering. For patients with recurrence or progression on first-line chemotherapy, we advocate enrollment in a clinical trial if one is available. For this reason, it may be beneficial to perform molecular testing in all patients with recurrent or progressive mucinous ovarian cancer.

Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review

OBJECTIVE

Over the past years, the application of artificial intelligence (AI) in medicine has increased rapidly, especially in diagnostics, and in the near future, the role of AI in medicine will become progressively more important. In this study, we elucidated the state of AI research on gynecologic cancers.

METHODS

A search was conducted in three databases-PubMed, Web of Science, and Scopus-for research papers dated between January 2010 and December 2020. As keywords, we used "artificial intelligence," "deep learning," "machine learning," and "neural network," combined with "cervical cancer," "endometrial cancer," "uterine cancer," and "ovarian cancer." We excluded genomic and molecular research, as well as automated pap-smear diagnoses and digital colposcopy.

RESULTS

Of 1632 articles, 71 were eligible, including 34 on cervical cancer, 13 on endometrial cancer, three on uterine sarcoma, and 21 on ovarian cancer. A total of 35 studies (49%) used imaging data and 36 studies (51%) used value-based data as the input data. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, cytology, and hysteroscopy data were used as imaging data, and the patients' backgrounds, blood examinations, tumor markers, and indices in pathological examination were used as value-based data. The targets of prediction were definitive diagnosis and prognostic outcome, including overall survival and lymph node metastasis. The size of the dataset was relatively small because 64 studies (90%) included less than 1000 cases, and the median size was 214 cases. The models were evaluated by accuracy scores, area under the receiver operating curve (AUC), and sensitivity/specificity. Owing to the heterogeneity, a quantitative synthesis was not appropriate in this review.

CONCLUSIONS

In gynecologic oncology, more studies have been conducted on cervical cancer than on ovarian and endometrial cancers. Prognoses were mainly used in the study of cervical cancer, whereas diagnoses were primarily used for studying ovarian cancer. The proficiency of the study design for endometrial cancer and uterine sarcoma was unclear because of the small number of studies conducted. The small size of the dataset and the lack of a dataset for external validation were indicated as the challenges of the studies.