Development and Validation of a Prognostic Nomogram for Extremity Soft Tissue Leiomyosarcoma

Prediction of 5-year survival in soft tissue leiomyosarcoma using a machine learning model algorithm

BACKGROUND AND OBJECTIVES

Leiomyosarcoma (LMS) is associated with one of the poorest overall survivals among soft tissue sarcomas. We sought to develop and externally validate a model for 5-year survival prediction in patients with appendicular or truncal LMS using machine learning algorithms.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used for development and internal validation of the models; external validation was assessed using our institutional database. Five machine learning algorithms were developed and then tested on our institutional database. Area under the receiver operating characteristic curve (AUC) and Brier score were used to assess model performance.

RESULTS

A total of 2209 patients from the SEER database and 81 patients from our tertiary institution were included. All models had excellent calibration with AUC 0.84-0.85 and Brier score 0.15-0.16. After assessing the performance indicators according to the TRIPOD model, we found that the Elastic-Net Penalized Logistic Regression outperformed other models. The AUCs of the institutional data were 0.83 (imputed) and 0.85 (complete-case analysis) with a Brier score of 0.16.

CONCLUSION

Our study successfully developed five machine learning algorithms to assess 5-year survival in patients with LMS. The Elastic-Net Penalized Logistic Regression retained performance upon external validation with an AUC of 0.85 and Brier score of 0.15.

A novel nomogram and recursive partitioning analysis for predicting cancer-specific survival of patients with subcutaneous leiomyosarcoma

Accurately predicting prognosis subcutaneous leiomyosarcoma (LMS) is crucial for guiding treatment decisions in patients. The objective of this study was to develop prediction models for cancer-specific survival (CSS) in patients with subcutaneous LMS. The collected cases of diagnosed subcutaneous LMS were randomly divided into a training cohort and a validation cohort at a 6:4 ratio based on tumor location and histological code. The X-tile program was utilized to determine the optimal cutoff points for age index. Univariate and Cox multivariate regression analyses were conducted to identify independent risk factors for subcutaneous LMS patients. Nomograms were constructed to predict CSS, and their performance was assessed using C-index and calibration plots. Additionally, a decision tree model was established using recursive partitioning analysis to determine the total score for CSS prediction in subcutaneous LMS patients based on the nomogram model. A total of 1793 patients with subcutaneous LMS were found. X-tile software divides all patients into ≤ 61 years old, 61-82 years old, and ≥ 82 years old. The most important anatomical sites were the limbs (including the upper and lower limbs, 48.0%). Only 6.2% of patients received chemotherapy, while 44% had a history of radiotherapy and 92.9% underwent surgery. The independent risk factors for patients with subcutaneous LMS were age, summary stage, grade, and surgery. CSS was significantly decreased in patients with distant metastases, which showed the highest independent risk predictor (HR 4.325, 95% CI 3.010-6.214, p < 0.001). The nomogram prediction model of LMS was constructed based on four risk factors. The C-index for this model was 0.802 [95% CI 0.781-0.823] and 0.798 [95% CI 0.768-0.829]. The training cohort's 3-, 5-, and 10-year AUCs for CSS in patients with subcutaneous LMS were 0.833, 0.830, and 0.859, and the validation cohort's AUC for predicting CSS rate were 0.849, 0.830, and 0.803, respectively. Recursive segmentation analysis divided patients into 4 risk subgroups according to the total score in the nomogram, including low-risk group < 145, intermediate-low-risk group ≥ 145 < 176, intermediate-high-risk group ≥ 176 < 196, and high-risk group ≥ 196; The probability of the four risk subgroups is 9.1%, 34%, 49%, and 79% respectively. In this retrospective study, a novel nomogram or corresponding risk classification system for patients with subcutaneous LMS were developed, which may assist clinicians in identifying high-risk patients and in guiding the clinical decision.

Prognostic value of mitotic count in leiomyosarcoma: A comprehensive monocentric retrospective study

BACKGROUND

Leiomyosarcomas (LMSs) include heterogeneous entities with different clinical courses not entirely predicted by known prognostic factors. In particular, the value of mitotic count as independent prognostic factor in LMS has been poorly investigated.

METHODS

We retrospectively analyzed all patients with a diagnosis of LMS who accessed to our Institution from June 1999 to May 2022 for which mitotic count was numerically expressed within the pathology report. Univariate and multivariate analyses were conducted to explore the prognostic value of mitotic count along with other clinical and histological variables.

RESULTS

We identified 121 eligible patients, with a median follow-up of 91.03 months (range 0.62-275.2 months). Median progression-free survival (mPFS) was 16.7 months, and median overall survival (mOS) was 105.6 months. In univariate analysis, mitotic count showed a significant impact on PFS and OS, with an hazard ratio per mitotic unit of 1.03 (1.01-1.04, p < 0.001) and 1.03 (1.01-1.04, p = 0.007), respectively. Similar results were found for locally advanced and metastatic patients, separately. Other significant prognostic factors for PFS were stage at diagnosis, performance status, tumor size and Ki-67, while differentiation, necrosis, grade, stage at diagnosis, tumor size, performance status and age at diagnosis were identified for OS. In multivariate analysis, the only significant factors were mitotic count and the presence of metastases at diagnosis for PFS, whereas the same two factors plus age at diagnosis were identified for OS.

CONCLUSION

Mitotic count represented the most important histological prognostic factor for OS and PFS in localized and metastatic LMS.

A web-based nomogram to predict overall survival for postresection leiomyosarcoma patients with lung metastasis

To investigate the overall survival of post-resection leiomyosarcoma (LMS) patients with lung metastasis, data of post-resection LMS patients with lung metastasis between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The clinical characteristics and survival data for post-resection LMS patients with lung metastasis at Tianjin Medical University Cancer Hospital & Institute (TJMUCH) between October 2010 and July 2018 were collected. Patients derived from the SEER database and TJMUCH were divided into training and validation cohorts, respectively. Univariate and multivariate Cox regression analyses were performed and a nomogram was established. The area under the curve (AUC) and the calibration curve were used to evaluate the nomogram. A web-based nomogram was developed based on the established nomogram. Eventually, 226 patients from the SEER database who were diagnosed with LMS and underwent primary lesion resection combined with lung metastasis were enrolled in the training cohort, and 17 patients from TJMUCH were enrolled in the validation cohort. Sex, race, grade, tumor size, chemotherapy, and bone metastasis were correlated with overall survival in patients with LMS. The C-index were 0.65 and 0.75 in the SEER and Chinese set, respectively. Furthermore, the applicable AUC values of the ROC curve in the SEER cohort to predict the 1-, 3-, 5- years survival rate were 0.646, 0.682, and 0.689, respectively. The corresponding AUC values in the Chinese cohort were 0.970, 0.913, and 0.881, respectively. The calibration curve showed that the nomogram performed well in predicting the overall survival in post-resection LMS patients with lung metastasis. A web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) was established. The web-based nomogram (https://weijunqiang.shinyapps.io/survival_lms_lungmet/) is an accurate and personalized tool for predicting the overall survival of post-resection LMS with lung metastasis.

Web-based nomograms for predicting overall survival and cancer-specific survival in retroperitoneal leiomyosarcoma: a population-based analysis

BACKGROUND

Retroperitoneal leiomyosarcoma is a type of carcinoma with low incidence and poor prognosis, and prognostic factors are currently unknown. Therefore, our study aimed to investigate the predictive factors of RPLMS and establish prognostic nomograms.

METHODS

Patients diagnosed with RPLMS between 2004 and 2017 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Prognostic factors were identified by univariate and multivariate COX regression analyses and used to generate nomograms to predict overall survival (OS) and cancer-specific survival (CSS).

RESULTS

646 eligible patients were randomly divided into training set (n = 323) and validation set (n = 323). Multivariate COX regression analysis indicated that the independent risk factors for OS and CSS were age, tumor size, grade, SEER stage, and surgery. In the nomogram of OS, the concordance indices (C-index) of the training and validation sets were 0.72 and 0.691, and in the nomogram of CSS, the C-indices of the training and validation sets were 0.737 and 0.737. Furthermore, calibration plots showed that the predicted results of the nomograms in the training and validation sets agree well with the actual observations.

CONCLUSION

Age, tumor size, grade, SEER stage, and surgery were independent prognostic factors for RPLMS. The nomograms developed and validated in this study can accurately predict the OS and CSS of patients, which could help clinicians make individualized survival predictions. Finally, we make the two nomograms into two web calculators for the convenience of clinicians.

Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients

Background

Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.

Methods

Patients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots.

Results

118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves.

Conclusion

The nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.

Web-based nomogram to predict postresection risk of distant metastasis in patients with leiomyosarcoma: retrospective analysis of the SEER database and a Chinese cohort

OBJECTIVES

This study investigated risk factors and constructed an online tool to predict distant metastasis (DM) risk in patients with leiomyosarcoma (LMS) after surgical resection.

METHODS

Data regarding patients with LMS who underwent surgical resection between 2010 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data were collected regarding patients with LMS who underwent surgical resection at Tianjin Medical University Cancer Hospital and Institute (TJMUCH) between October 2010 and July 2018. Patients were randomly divided into training and validation sets. Logistic regression analyses were performed; a nomogram was established. The area under the curve (AUC) and calibration curve were used to evaluate the nomogram, which served as the basis for a web-based nomogram.

RESULTS

This study included 4461 and 76 patients from the SEER database and TJMUCH, respectively. Age, ethnicity, grade, T stage, N stage, radiotherapy, and chemotherapy were associated with DM incidence. C-index values were 0.815 and 0.782 in the SEER and Chinese datasets, respectively; corresponding AUC values were 0.814 and 0.773, respectively. A web-based nomogram (https://weijunqiang-leimyosarcoma-seer.shinyapps.io/dynnomapp/) was established.

CONCLUSIONS

Our web-based nomogram is an accurate and user-friendly tool to predict DM risk in patients with LMS; it can aid clinical decision-making.

Comparison of nomogram with random survival forest for prediction of survival in patients with spindle cell carcinoma

Purpose

Spindle cell carcinoma (SpCC) is a relatively rare tumor with an unfavorable prognosis. This study aimed to develop and validate a prediction model for the individual survival of patients with SpCC using Cox regression and the random survival forest (RSF) model.

Methods

Patients diagnosed with SpCC between 2004 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided into training and validating cohorts. Cox regression and RSF were used to identify prognostic predictors and build prediction models. A nomogram based on Cox regression was constructed to predict the 1-, 3-, and 5-year survival of patients with SpCC. Internal validation was conducted using the bootstrapping method. We evaluated the discrimination accuracy and calibration of the model using Harrell's C-index and calibration plot, respectively.

Results

Two hundred and fifty patients diagnosed with SpCC with required information were enrolled in this study. Multivariate Cox regression and RSF identified age, primary site, grade, SEER stage, tumor size, and treatment as significant prognostic predictors of SpCC. The bootstrapped and validated C-indices were 0.812 and 0.783 for nomogram, and 0.790 and 0.768 for RSF, respectively. Calibration plot of the nomogram showed an agreement between the prediction and actual observation.

Conclusions

The nomogram developed in this study is a promising tool with a simplified presentation that can easily be used and interpreted by clinicians for evaluating the survival of each patient with SpCC; its performance was comparable to that of RSF. Application of such models are needed to help oncologists identify the high-risk patients and improve clinical decision making of SpCC treatment.

Circulating Tumor DNA Is Associated with Response and Survival in Patients with Advanced Leiomyosarcoma

PURPOSE

We sought to determine whether the detection of circulating tumor DNA (ctDNA) in samples of patients undergoing chemotherapy for advanced leiomyosarcoma (LMS) is associated with objective response or survival.

EXPERIMENTAL DESIGN

Using ultra-low-passage whole-genome sequencing (ULP-WGS) of plasma cell-free DNA from patients treated on a prospective clinical trial, we tested whether detection of ctDNA evaluated prior to the start of therapy and after two cycles of chemotherapy was associated with treatment response and outcome. Associations between detection of ctDNA and pathologic measures of disease burden were evaluated.

RESULTS

We found that ctDNA was detectable by ULP-WGS in 49% patients prior to treatment and in 24.6% patients after two cycles of chemotherapy. Detection of pretreatment ctDNA was significantly associated with a lower overall survival [HR, 1.55; 95% confidence interval (CI), 1.03-2.31; P = 0.03] and a significantly lower likelihood of objective response [odds ratio (OR), 0.21; 95% CI, 0.06-0.59; P = 0.005]. After two cycles of chemotherapy, patients who continued to have detectable levels of ctDNA experienced a significantly worse overall survival (HR, 1.77; 95% CI, 1-3.14; P = 0.05) and were unlikely to experience an objective response (OR, 0.05; 95% CI, 0-0.39; P = 0.001).

CONCLUSIONS

Our results demonstrate that detection of ctDNA is associated with outcome and objective response to chemotherapy in patients with advanced LMS. These results suggest that liquid biopsy assays could be used to inform treatment decisions by recognizing patients who are likely and unlikely to benefit from chemotherapy. See related commentary by Kasper and Wilky, p. 2480.

Prognosticators and Prognostic Nomograms for Leiomyosarcoma Patients With Metastasis

Individual survival prediction and risk stratification are of vital importance to optimize the individualized treatment of metastatic leiomyosarcoma (LMS) patients. This study aimed to identify the prognostic factors for metastatic LMS patients and establish prognostic models for overall survival (OS) and cancer-specific survival (CSS). The data of LMS patients with metastasis between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The entire cohort was randomly divided into a training cohort and a validation cohort. The influences of primary tumor site, localized and distant metastases, and sites and number of metastases on the prognosis of metastatic LMS patients were firstly explored by Kaplan–Meier curves and log-rank tests. Furthermore, the effective therapeutic regimens and prognosticators for metastatic LMS patients were also analyzed by Cox analysis. In addition, two prognostic nomograms for OS and CSS were established, and their predictive performances were evaluated by the methods of receiver operating characteristic (ROC) curves, time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). A total of 498 patients were finally collected from the SEER database and were randomly assigned to the training set (N = 332) and validation set (N = 166). No significant differences in OS were observed in patients with distant organ metastasis and localized metastasis. For patients who have already developed distant organ metastasis, the sites and number of metastases seemed to be not closely associated with survival. Patients who received chemotherapy got significantly longer survival than that of their counterparts. In univariate and multivariate Cox analyses, variables of surgery, chemotherapy, age, and tumor size were identified as independent predictors for OS and CSS, and distant metastasis was also independently associated with CSS. The areas under the curve (AUCs) of ROC curves of the nomogram for predicting 1-, 3-, and 5-year OS were 0.770, 0.800, and 0.843, respectively, and those for CSS were 0.777, 0.758, and 0.761, respectively. The AUCs of time-dependent AUCs were all over 0.750. The calibration curves and DCA curves also showed excellent performance of the prognostic nomograms. Metastasis is associated with reduced survival, while the sites and the number of metastases are not significantly associated with survival. The established nomogram is a useful tool that can help to perform survival stratification and to optimize prognosis-based decision-making in clinical practice.

Construction, validation and, visualization of a web-based nomogram for predicting the overall survival and cancer-specific survival of leiomyosarcoma patients with lung metastasis

Background

This study sought to assess the prognostic factors for leiomyosarcoma (LMS) patients with lung metastasis and construct web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS).

Method

Patients diagnosed with LMS combined with lung metastasis between 2010 and 2016 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into a training set and a testing set. The X-tile analysis provides the best age and tumor size cut-off point, and changes continuous variables into categorical variables. The independent prognostic factors were determined by Cox regression analysis, and 2 nomograms were established. Receiver operating characteristic curves and calibration curves were used to evaluate the nomograms. Based on the nomograms, 2 web-based nomograms were established.

Results

Two hundred and twenty-eight cases were included in the OS nomogram construction, and were randomly divided into a training set (n=160) and a validation set (n=68). Age, T stage, bone metastasis, surgery, chemotherapy, marital status, tumor size, and tumor site were found to be correlated with OS. One hundred and eighty-three cases were enrolled in the CSS nomogram construction, and randomly divided into a training set (n=129) and a validation set (n=54). Age, bone metastasis, surgery, chemotherapy, tumor size, and tumor site were found to be correlated with CSS. Two nomograms were established to predict OS and CSS. In the training set, the areas under the curve of the nomogram for predicting 1-, 2-, and 3-year OS were 0.783, 0.830, and 0.832, respectively, and those for predicting 1-, 2-, and 3-year CSS were 0.889, 0.777, and 0.884, respectively. Two web-based nomograms were established to predict OS (https://wenn23.shinyapps.io/lmslmosapp/), and CSS (https://wenn23.shinyapps.io/lmslmcssapp/).

Conclusion

The developed web-based nomogram is a useful tool for accurately analyzing the prognosis of LMS patients with lung metastasis, and could help clinical doctors to make personalized clinical decisions.

NKX6-1 mediates cancer stem-like properties and regulates sonic hedgehog signaling in leiomyosarcoma

Background

Leiomyosarcoma (LMS), the most common soft tissue sarcoma, exhibits heterogeneous and complex genetic karyotypes with severe chromosomal instability and rearrangement and poor prognosis.

Methods

Clinical variables associated with NKX6-1 were obtained from The Cancer Genome Atlas (TCGA). NKX6-1 mRNA expression was examined in 49 human uterine tissues. The in vitro effects of NXK6-1 in LMS cells were determined by reverse transcriptase PCR, western blotting, colony formation, spheroid formation, and cell viability assays. In vivo tumor growth was evaluated in nude mice.

Results

Using The Cancer Genome Atlas (TCGA) and human uterine tissue datasets, we observed that NKX6-1 expression was associated with poor prognosis and malignant potential in LMS. NKX6-1 enhanced in vitro tumor cell aggressiveness via upregulation of cell proliferation and anchorage-independent growth and promoted in vivo tumor growth. Moreover, overexpression and knockdown of NKX6-1 were associated with upregulation and downregulation, respectively, of stem cell transcription factors, including KLF8, MYC, and CD49F, and affected sphere formation, chemoresistance, NOTCH signaling and Sonic hedgehog (SHH) pathways in human sarcoma cells. Importantly, treatment with an SHH inhibitor (RU-SKI 43) but not a NOTCH inhibitor (DAPT) reduced cell survival in NKX6-1-expressing cancer cells, indicating that an SHH inhibitor could be useful in treating LMS. Finally, using the TCGA dataset, we demonstrated that LMS patients with high expression of NKX6-1 and HHAT, an SHH pathway acyltransferase, had poorer survival outcomes compared to those without.

Conclusions

Our findings indicate that NKX6-1 and HHAT play critical roles in the pathogenesis of LMS and could be promising diagnostic and therapeutic targets for LMS patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12929-021-00726-6.

Leiomyosarcoma of the urinary bladder: A SEER database study and comparison to leiomyosarcomas of the uterus and extremities/trunk

No well-established staging system exists for bladder leiomyosarcoma (LMS), and the current staging system does not include tumor size, a thoroughly validated prognostic parameter for sarcomas. Uterine and extremity/trunk LMS are more common than those in the bladder and have well-established staging systems incorporating tumor size. We aim to improve the understanding of LMS of the urinary bladder by assessing cancer-specific survival (CSS) and comparing LMS at this unusual anatomic site to those arising at other sites using the Surveillance, Epidemiology, and End Results (SEER) database. The SEER database (1973-2013) was queried for bladder, uterus, and trunk/extremity LMS. Multivariable Cox proportional hazard regression was performed to identify predictors of CSS for each anatomic location and used to compare outcomes at different sites. We identified 165 bladder, 4987 uterus, and 2536 extremity/trunk LMS cases. Five-year CSS was 52% for uterus, 73% for bladder, and 82% for extremity/trunk LMS. For LMS at all sites, uterine location (HR = 2.14, P < 0.001) and increasing tumor size (HR = 1.05, P < 0.001) were significant predictors of worse CSS on multivariate analysis. For bladder LMS, increasing tumor size (HR = 1.18, P = 0.003) was an independent prognostic factor and the conventional staging cut-off threshold of 5 cm for sarcomas outside the head/neck showed statistical significance in stratifying patient risk of cancer-related death. Bladder LMS appears to have clinical behavior intermediate between those of the extremities/trunk and uterus. We suggest that the conventional sarcoma staging protocols based on tumor size be applied to LMS of the urinary bladder.

Peritoneal cancer index (PCI) based patient selecting strategy for complete cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy in gastric cancer with peritoneal metastasis: A single-center retrospective analysis of 125 patients

OBJECTIVE

The role of cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC) in gastric cancer with peritoneal metastasis (GCPM) is still controversial, mainly due to the limited survival benefit and uncertain patient selection. This study aims to construct a selecting strategy in GCPM for CRS + HIPEC.

METHODS

From a prospective established database, 125 patients were enrolled. All these patients were pathologically confirmed as GCPM and treated with CRS + HIPEC with or without preoperative or postoperative chemotherapy. The clinical documents and follow-up results were collected and analyzed with the primary endpoint of overall survival (OS) and the secondary endpoint of perioperative serious adverse events (SAEs).

RESULTS

The median OS of 125 GCPM patients treated with CRS + HIPEC was 10.7 months, with 1-, 2-, 3-, and 5-year survival rates of 43.8%, 24.7%, 18.6%, and 15.7%, respectively. The multivariate analysis identified completeness of cytoreduction (CC), SAEs, HIPEC drugs, and adjuvant chemotherapy as independent prognostic factors on OS. The median OS was 30.0 (95%CI: 16.8-43.3) months in CC-0 group, significantly better than 7.3 (95%CI: 5.8-8.8) months in CC1-3 group (P < 0.001). The median OS showed no significant difference among CC-1 (8.5, 95%CI: 6.7-10.2, months), CC-2 (5.6, 95%CI: 3.0-8.2, months) and CC-3 (6.5, 95%CI: 5.2-7.7, months) groups (P > 0.05 for all pairwise comparations). The nomogram based on peritoneal metastasis timing, preoperative tumor marker (TM), and peritoneal cancer index (PCI), with AUC of 0.985, showed a good accuracy and consistency between actual observation and prediction of the probability of complete CRS. The cutoffs of PCI were 16 for synchronous GCPM with normal TM, 12 for synchronous GCPM with abnormal TM, 10 for metachronous GCPM with normal TM, and 5 for metachronous GCPM with abnormal TM, setting the probability to achieve complete CRS as 50%.

CONCLUSIONS

Only complete CRS + HIPEC (CC-0) could improve survival for high selected GCPM patients with acceptable safety. An incomplete CRS (CC1-3) should be avoided for GCPM patients. Synchronous GCPM with PCI ≤16 and normal TM, synchronous GCPM with PCI ≤12 and abnormal TM, metachronous GCPM with PCI ≤10 and normal TM, or metachronous GCPM with PCI ≤5 and abnormal TM maybe potential indications for complete CRS + HIPEC treatment.

Novel prognostic nomograms for female patients with breast cancer and bone metastasis at presentation

Background

There is a paucity of literature about prognostic evaluation for patients with breast cancer (BC) and bone metastasis at presentation. To date, little is known about how to accurately predict the prognosis of BC patients with bone metastasis at presentation. Thus, an accurate prediction tool of prognosis in this population is urgently needed. Our goal is to construct novel and prognostic nomograms for BC patients with bone metastasis at presentation.

Methods

We searched Surveillance, Epidemiology, and End Results (SEER) database for BC patients with bone metastasis at presentation between 2010 and 2016. Multivariate analysis was performed to obtain significantly independent variables. Then, novel prognostic nomograms were constructed based on those independent predictors.

Results

Tumor grade, histological type, primary tumor size, tumor subtype, surgery, chemotherapy and number of metastatic organs except bone were recognized as significantly independent variables of both overall survival (OS) and cancer-specific survival (CSS). Then those significant variables were integrated to construct nomograms for 3- and 5-year survival. Calibration plots for the 3- and 5-year survival in training and validation sets showed that the prediction curve was close to a 45 degree slash. The C-indices of OS in training and validation cohorts were 0.705 and 0.678, respectively. Similar results were observed for CSS in training and validation cohorts.

Conclusions

Our proposed nomograms can effectively and accurately predict the prognosis of BC patients with bone metastasis at presentation, which provide a basis for individual treatments for metastatic lesions.

Personalizing age-specific survival prediction and risk stratification in intracranial grade II/III ependymoma

BACKGROUND

Models for estimation of survival rates of patients with intracranial grade II/III ependymoma (EPN) are scarce. Considering the heterogeneity in prognostic factors between pediatric and adult patients, we aimed to develop age-specific nomograms for predicting 3-, 5-, and 8-year survival for these patients.

METHODS

A total of 1390 cases (667 children; 723 adults) of intracranial grade II/III EPNs diagnosed between 1988 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database for our study. Univariable and multivariable Cox analyses were employed to identify independent prognostic predictors. Age-specific nomograms were developed based on the results of multivariate Cox analyses. We also evaluated the performance of these predictive models by concordance index, calibration curves, time-dependent receiver operating characteristic curves, and decision curve analyses.

RESULTS

Considerable heterogeneity in prognostic factors was highlighted between pediatric and adult patients. Age, sex, tumor grade, surgery treatment and radiotherapy were identified as significant predictors of overall survival for children, and age, tumor grade, tumor size, surgery treatment, and marital status for adult. Based on these factors, age-specific nomogram models were established and internally validated. These models exhibited favorable discrimination and calibration characteristics. Nomogram-based risk classification systems were also constructed to facilitate risk stratification in EPNs for optimization of clinical management.

CONCLUSIONS

We developed the first nomograms and corresponding risk classification systems for predicting survival in patients with intracranial grade II/III EPN. These easily used tools can assist oncologists in making accurate survival evaluation.