scholarly journals NCC-BCBM, A Nomogram Prognostic Model In Breast Cancer Patients With Bone Metastases

2021 ◽  
Author(s):  
Jie Zhai ◽  
Qiang Liu ◽  
Ping Bai ◽  
Zhongzhao Wang ◽  
Yi Fang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Calibration Curve ◽  
Prognostic Model ◽  
Predictive Accuracy ◽  
Molecular Subtype ◽  
Breast Cancer Patients ◽  
Discriminative Ability ◽  
Independent Cohort

Abstract Accurate prediction tools to facilitate risk stratification and therapeutic strategies for breast cancer patients with bone metastasis (BCBM) are lacking. We constructed and validated a new nomogram prognostic model, named NCC-BCBM, for breast cancer patients with bone metastasis using a large BCBM cohort from the SEER database. Clinical information for 8655 patients diagnosed from 2011 to 2013 was collected to develop the model. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve. The model was further validated in an independent cohort of 4634 BCBM patient. The following clinical variables were enrolled in the final prognostic model: age, race, surgery, radiation therapy, chemotherapy, laterality, grade, molecular subtype, American Joint Committee on Cancer (AJCC T) stage, AJCC N stage and extra metastatic sites except bone. The C-index for the developed model in training cohort was 0.702 (95% CI, 0.696 to 0.709). The calibration curve for probability of 1-year, 3-year and 5-year survival showed good agreement between prediction by nomogram and direct observation. C-index that validated in an independent cohort was 0.748 (95% CI, 0.737 to 0.759). We developed and validated a nomogram prognostic model for BCBM patients and it resulted in good performance.

scholarly journals NCC-BCBM, a Nomogram Prognostic Model in Breast Cancer Patients with Bone Metastases

2021 ◽  
Author(s):  
Jie Zhai ◽  
Qiang Liu ◽  
Ping Bai ◽  
Zhongzhao Wang ◽  
Yi Fang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Calibration Curve ◽  
Prognostic Model ◽  
Predictive Accuracy ◽  
Molecular Subtype ◽  
Concordance Index ◽  
Breast Cancer Patients ◽  
Discriminative Ability

Abstract Purpose Accurate prediction tools to facilitate risk stratification and therapeutic strategies for breast cancer patients with bone metastasis (BCBM) are lacking. We constructed and validated a new nomogram prognostic model, named NCC-BCBM, for breast cancer patients with bone metastasis using a large BCBM cohort from the SEER (Surveillance Epidemiology and End Results) database. Patients and Methods Clinical information for 8655 patients diagnosed from 2011 to 2013 was collected to develop the nomogram prognostic model. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C-index) and calibration curve. The model was further validated in an independent cohort of 4634 BCBM patients diagnosed from 2014 to 2015. Results The following clinical variables were enrolled in the final prognostic model: age, race, surgery, radiation therapy, chemotherapy, laterality, grade, molecular subtype, American Joint Committee on Cancer (AJCC T) stage, AJCC N stage and extra metastatic sites except bone. The C-index for the developed model in training cohort was 0.702 (95% CI, 0.696 to 0.709). The calibration curve for probability of 1-year, 3-year and 5-year survival showed good agreement between prediction by nomogram and direct observation. The prognostic model was validated in an independent validation cohort with a concordance index of 0.748 (95% CI, 0.737 to 0.759). Conclusion We developed and validated a nomogram prognostic model for BCBM patients and the proposed nomogram resulted in good performance.

scholarly journals NCCBM, a Nomogram Prognostic Model in Breast Cancer Patients With Brain Metastasis

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiang Liu ◽  
Xiangyi Kong ◽  
Zhongzhao Wang ◽  
Xiangyu Wang ◽  
Wenxiang Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Brain Metastasis ◽  
Cancer Patients ◽  
Calibration Curve ◽  
Prognostic Model ◽  
Predictive Accuracy ◽  
Molecular Subtype ◽  
Prognostic Models ◽  
Breast Cancer Patients ◽  
Discriminative Ability

Purpose: Nomogram prognostic models could greatly facilitate risk stratification and treatment strategies for cancer patients. We developed and validated a new nomogram prognostic model, named NCCBM, for breast cancer patients with brain metastasis (BCBM) using a large BCBM cohort from the SEER (Surveillance, Epidemiology, and End Results) database.Patients and Methods: Clinical data for 975 patients diagnosed from 2011 to 2014 were used to develop the nomogram prognostic model. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The results were validated using an independent cohort of 542 BCBM patients diagnosed from 2014 to 2015.Results: The following variables were selected in the final prognostic model: age, race, surgery, radiation therapy, chemotherapy, laterality, grade, molecular subtype, and extracranial metastatic sites. The C-index for the model described here was 0.69 (95% CI, 0.67 to 0.71). The calibration curve for probability of survival showed good agreement between prediction by nomogram and actual observation. The model was validated in an independent validation cohort with a C-index of 0.70 (95% CI, 0.68 to 0.73).Conclusion: We developed and validated a nomogram prognostic model for BCBM patients, and the proposed nomogram resulted in good performance.

scholarly journals Karakteristik Klinikopatologik Pasien Kanker Payudara dengan Metastasis Tulang di RSUP Sanglah pada Tahun 2014 - 2018

e-CliniC ◽  
2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Putu Krishna B. S. Putra ◽  
I Wayan J. Sumadi ◽  
Ni Putu Sriwidyani ◽  
IG Budhi Setiawan
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Invasive Carcinoma ◽  
Molecular Subtype ◽  
Metastatic Breast ◽  
Breast Cancer Patients ◽  
Luminal B ◽  
Histopathological Type

Abstract: Breast cancer is the most common cancer in woman. Metastasis often occurs especially to the bones. This study was aimed to determine the characteristics of breast cancer patients with bone metastasis. This was a descriptive study with a cross-sectional design. Samples were 46 breast cancer patients with bone metastasis recorded at Sanglah Hospital from 2014 until 2018. Data of pathological examination archives of Oncology Surgery Division Medical Faculty of Udayana University/Sanglah General Hospital were used to obtain the clinicopathological characteristics of metastatic breast cancer patients based on age, lateralization, histopathological type, and tumor molecular subtype. The results showed that most cases of metastatic breast cancer were aged 40-49 years as many 21 patients (45.7%), minimal difference in lateralization between right breast as many 22 patients (47.8%) and left breast 23 patients (50%). The most common histopathological type was invasive carcinoma of no special type as many 34 patients (73.9%). The most common tumor subtype was the luminal B subtype as many 21 patients (45.7%). In conclusion, most patients of breast cancer with bone metastasis were 40-49 years old, invasive carcinoma of no special type, molecular subtype of luminal B, and no significant difference between lateralization to the right and left breast.Keywords: breast cancer, bone, metastasis, clinicopathological caharacteristics Abstrak: Kanker payudara merupakan jenis kanker yang paling sering dijumpai pada wanita. Metastasis sering terjadi terutama pada tulang. Penelitian ini bertujuan untuk mengetahui karakteristik pasien kanker payudara dengan metastasis tulang di RSUP Sanglah Denpasar. Jenis penelitian ialah deskriptif dengan desain potong lintang. Sampel penelitian ialah 46 pasien kanker payudara dengan metastasis tulang yang tercatat di RSUP Sanglah tahun 2014-2018. Data diambil dari arsip hasil pemeriksaan patologi di Subdivisi Bedah Onkologi, Departemen/Kelompok Staf Medis (KSM) Bedah Fakultas Kedokteran Universitas Udayana (FK UNUD)/RSUP Sanglah untuk mendapatkan karakteristik klinikopatologi pasien kanker payudara metastasis tulang berdasarkan usia, lateralisasi, tipe histopatologik, dan subtipe molekuler tumor. Hasil penelitian menunjukkan kasus terbanyak terjadi pada rentang usia 40-49 tahun sebanyak 21 orang (45,7%), dengan lateralisasi tidak jauh berbeda antara payudara kanan sebanyak 22 orang (47,8) dan kiri sebanyak 23 orang (50%). Tipe histopatologik yang lebih sering ditemukan yaitu invasive carcinoma of no special type sebanyak 34 orang (73,9%). Subtipe molekuler yang paling banyak ditemukan ialah subtipe luminal B sebanyak 21 orang (45,7%). Simpulan penelitian ini pasien kanker payudara dengan metastasis tulang berada pada rentang usia 40-49 tahun, invasive carcinoma of no special type, subtipe molekuler luminal B. dan lateralisasi payudara kanan dan kiri tidak jauh berbeda.Kata kunci: kanker payudara, metastasis, tulang, karakteristik klinikopatologik

scholarly journals Karakteristik Klinikopatologik Pasien Kanker Payudara dengan Metastasis Tulang di RSUP Sanglah pada Tahun 2014 - 2018

e-CliniC ◽  
2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Putu Krishna B. S. Putra ◽  
I Wayan J. Sumadi ◽  
Ni Putu Sriwidyani ◽  
IG Budhi Setiawan
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Invasive Carcinoma ◽  
Molecular Subtype ◽  
Metastatic Breast ◽  
Breast Cancer Patients ◽  
Luminal B ◽  
Histopathological Type

Abstract: Breast cancer is the most common cancer in woman. Metastasis often occurs especially to the bones. This study was aimed to determine the characteristics of breast cancer patients with bone metastasis. This was a descriptive study with a cross-sectional design. Samples were 46 breast cancer patients with bone metastasis recorded at Sanglah Hospital from 2014 until 2018. Data of pathological examination archives of Oncology Surgery Division Medical Faculty of Udayana University/Sanglah General Hospital were used to obtain the clinicopathological characteristics of metastatic breast cancer patients based on age, lateralization, histopathological type, and tumor molecular subtype. The results showed that most cases of metastatic breast cancer were aged 40-49 years as many 21 patients (45.7%), minimal difference in lateralization between right breast as many 22 patients (47.8%) and left breast 23 patients (50%). The most common histopathological type was invasive carcinoma of no special type as many 34 patients (73.9%). The most common tumor subtype was the luminal B subtype as many 21 patients (45.7%). In conclusion, most patients of breast cancer with bone metastasis were 40-49 years old, invasive carcinoma of no special type, molecular subtype of luminal B, and no significant difference between lateralization to the right and left breast.Keywords: breast cancer, bone, metastasis, clinicopathological caharacteristics Abstrak: Kanker payudara merupakan jenis kanker yang paling sering dijumpai pada wanita. Metastasis sering terjadi terutama pada tulang. Penelitian ini bertujuan untuk mengetahui karakteristik pasien kanker payudara dengan metastasis tulang di RSUP Sanglah Denpasar. Jenis penelitian ialah deskriptif dengan desain potong lintang. Sampel penelitian ialah 46 pasien kanker payudara dengan metastasis tulang yang tercatat di RSUP Sanglah tahun 2014-2018. Data diambil dari arsip hasil pemeriksaan patologi di Subdivisi Bedah Onkologi, Departemen/Kelompok Staf Medis (KSM) Bedah Fakultas Kedokteran Universitas Udayana (FK UNUD)/RSUP Sanglah untuk mendapatkan karakteristik klinikopatologi pasien kanker payudara metastasis tulang berdasarkan usia, lateralisasi, tipe histopatologik, dan subtipe molekuler tumor. Hasil penelitian menunjukkan kasus terbanyak terjadi pada rentang usia 40-49 tahun sebanyak 21 orang (45,7%), dengan lateralisasi tidak jauh berbeda antara payudara kanan sebanyak 22 orang (47,8) dan kiri sebanyak 23 orang (50%). Tipe histopatologik yang lebih sering ditemukan yaitu invasive carcinoma of no special type sebanyak 34 orang (73,9%). Subtipe molekuler yang paling banyak ditemukan ialah subtipe luminal B sebanyak 21 orang (45,7%). Simpulan penelitian ini pasien kanker payudara dengan metastasis tulang berada pada rentang usia 40-49 tahun, invasive carcinoma of no special type, subtipe molekuler luminal B. dan lateralisasi payudara kanan dan kiri tidak jauh berbeda.Kata kunci: kanker payudara, metastasis, tulang, karakteristik klinikopatologik

COX-2 expression in bone metastasis of breast cancer patients

2007 ◽  
Vol 4 (02) ◽  
Author(s):  
LC Horn ◽  
A Meinel ◽  
C Pleul ◽  
C Leo ◽  
P Wuttke
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Cox 2

scholarly journals Surgery for primary tumor benefits survival for breast cancer patients with bone metastases: a large cohort retrospective study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhangheng Huang ◽  
Xin Zhou ◽  
Yuexin Tong ◽  
Lujian Zhu ◽  
Ruhan Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Bone Metastases ◽  
Cancer Patients ◽  
Primary Tumor ◽  
Predictive Accuracy ◽  
Rank Test ◽  
Breast Cancer Patients ◽  
Risk Of Death ◽  
The Impact

Abstract Background The role of surgery for the primary tumor in breast cancer patients with bone metastases (BM) remains unclear. The purpose of this study was to determine the impact of surgery for the primary tumor in breast cancer patients with BM and to develop prognostic nomograms to predict the overall survival (OS) of breast cancer patients with BM. Methods A total of 3956 breast cancer patients with BM from the Surveillance, Epidemiology, and End Results database between 2010 and 2016 were included. Propensity score matching (PSM) was used to eliminate the bias between the surgery and non-surgery groups. The Kaplan-Meier analysis and the log-rank test were performed to compare the OS between two groups. Cox proportional risk regression models were used to identify independent prognostic factors. Two nomograms were constructed for predicting the OS of patients in the surgery and non-surgery groups, respectively. In addition, calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the performance of nomograms. Result The survival analysis showed that the surgery of the primary tumor significantly improved the OS for breast cancer patients with BM. Based on independent prognostic factors, separate nomograms were constructed for the surgery and non-surgery groups. The calibration and ROC curves of these nomograms indicated that both two models have high predictive accuracy, with the area under the curve values ≥0.700 on both the training and validation cohorts. Moreover, DCA showed that nomograms have strong clinical utility. Based on the results of the X-tile analysis, all patients were classified in the low-risk-of-death subgroup had a better prognosis. Conclusion The surgery of the primary tumor may provide survival benefits for breast cancer patients with BM. Furthermore, these prognostic nomograms we constructed may be used as a tool to accurately assess the long-term prognosis of patients and help clinicians to develop individualized treatment strategies.

scholarly journals Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 518
Author(s):  
Da-Chuan Cheng ◽  
Te-Chun Hsieh ◽  
Kuo-Yang Yen ◽  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Deep Learning ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Breast Cancer Patients ◽  
Location Detection ◽  
The Mean ◽  
Mean Sensitivity

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

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