scholarly journals Tumor marker based survival analysis for patients with pseudomyxoma peritonei of appendiceal origin: A retrospective cohort study

2020 ◽  
Author(s):  
Mingjian Bai ◽  
Shilong Wang ◽  
Ruiqing Ma ◽  
Ying Cai ◽  
Yiyan Lu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pseudomyxoma Peritonei ◽  
Prediction Models ◽  
Radical Surgery ◽  
Cox Model ◽  
Univariate Analysis ◽  
Rank Test ◽  
Internal Validation ◽  
Discrimination Ability ◽  
D Dimer

Abstract Background Pseudomyxoma peritonei (PMP) is a rare disease, the prognosis of overall survival (OS) is affected by many factors, present study aim to screen independent prediction indicators and establish a nomogram for PMP. Methods 119 PMP patients received cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) in our center for the first time were included between 01/06/2013 and 22/11/2019 . The log-rank test was used to compare the OS rate among groups, subsequently, variables with P<0.10 were subjected to multivariate Cox model for screening independent prediction indicators. Finally, the nomogram prediction models will be established. Results Univariate analysis showed that Barthel Index Score, Albumin, D-Dimer, CEA, CA125, CA19-9, CA724, CA242, PCI, degree of radical surgery, histopathological grade were significant predictors for OS in PMP. At multivariate analysis, Sex, D-Dimer, CA125, CA19-9, PCI, and degree of radical surgery were independently associated with OS rate in PMP. A nomogram was plotted based on the independent predictive factors for PMP and undergone internal validation, ROC analysis was performed to calculate discrimination ability of prediction model, the area under curves (AUC) was 0.880 (95% CI : 0.806- 0.933). Conclusions Several factors (Sex, D-Dimer, CA125, CA19-9, PCI, and degree of radical surgery) have independent prognostic value for survival in PMP, the tumor based prediction model has a better prediction value, more researches are need to verify and improve the prediction model.

scholarly journals Tumor marker based survival analysis for patients with pseudomyxoma peritonei of appendiceal origin: A retrospective cohort study

2020 ◽  
Author(s):  
Mingjian Bai ◽  
Shilong Wang ◽  
Ruiqing Ma ◽  
Ying Cai ◽  
Yiyan Lu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pseudomyxoma Peritonei ◽  
Roc Analysis ◽  
Prediction Models ◽  
Radical Surgery ◽  
Univariate Analysis ◽  
Barthel Index Score ◽  
Discrimination Ability ◽  
First Time ◽  
D Dimer

Abstract Background Pseudomyxoma peritonei (PMP) is a rare disease, the prognosis of overall survival (OS) is affected by many factors, present study aim to screen independent prediction indicators for PMP and establish prediction model for OS rates in PMP.Methods 119 PMP patients received cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) in our center for the first time were included between 01/06/2013 and 22/11/2019 . The log-rank test was used to compare the OS rate among groups, subsequently, variables with P<0.10 were subjected to multivariate Cox model for screening independent prediction indicators. Finally, the prediction models for OS in PMP will be established.Results Univariate analysis showed that Barthel Index Score, albumin, D-dimer, CEA, CA125, CA19-9, CA724, CA242, PCI, degree of radical surgery, histopathological grade were significant predictors for OS in PMP. At multivariate analysis, sex, D-dimer, CA125, CA19-9, and degree of radical surgery were independently associated with OS rate in PMP. ROC analysis was performed to calculate discrimination ability of prediction model and the area under curves (AUC) was 0.902 (95%CI: 0.823-0.954). Finally, nomogram was plotted by the independent predictive factors for PMP.Conclusions Several factors (sex, degree of radical surgery, D-dimer, preoperative CA125 and CA19-9) have independent prognostic value for survival in PMP, the tumor based prediction model has better prediction value, more researches are need to verify and improve the prediction model.

scholarly journals Nomogram to predict overall survival for patients with pseudomyxoma peritonei of appendiceal origin: A retrospective cohort study

2020 ◽  
Author(s):  
Mingjian Bai ◽  
Shilong Wang ◽  
Ruiqing Ma ◽  
Ying Cai ◽  
Yiyan Lu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Pseudomyxoma Peritonei ◽  
Roc Analysis ◽  
Prediction Models ◽  
Radical Surgery ◽  
Individual Risk ◽  
Good Discrimination ◽  
Internal Validation ◽  
Discrimination Ability ◽  
First Time

Abstract Background Pseudomyxoma peritonei (PMP) is a rare disease, the prognosis of overall survival (OS) is affected by many factors, present study aim to define independent prediction indicators and establish a nomogram for PMP patients.Methods 119 PMP patients received cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) in our center for the first time were included between 01/06/2013 and 22/11/2019 . The log-rank test was used to compare the OS rate among groups, subsequently, variables with P<0.10 were subjected to multivariate Cox model for defining independent prediction indicators. Finally, the nomogram prediction models will be established and for internal validation.Results Multivariate analysis showed Sex, D-Dimer, CA125, CA19-9, PCI, and degree of radical surgery were independently associated with OS in PMP patients. A nomogram was plotted based on the independent predictive factors and undergone internal validation, ROC analysis was performed to calculate discrimination ability of the nomogram, the C-index was 0.880 (95%CI: 0.806- 0.933) and calibration plots showed good performance. Conclusions Six independent prognostic factor for predicting survival in PMP patients were difined, the nomogram has a good discrimination ability for individual risk predition, more researches are needed to verify and improve the prediction model.

scholarly journals Treatment success of internet-based vestibular rehabilitation in general practice: development and internal validation of a prediction model

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e038649
Author(s):  
Vincent A van Vugt ◽  
Martijn W Heymans ◽  
Johannes C van der Wouden ◽  
Henriëtte E van der Horst ◽  
Otto R Maarsingh
Keyword(s):  
General Practice ◽  
The Netherlands ◽  
Prediction Model ◽  
Predictive Models ◽  
Prediction Models ◽  
Short Form ◽  
Treatment Success ◽  
Vestibular Rehabilitation ◽  
Internal Validation ◽  
Vestibular Symptoms

ObjectivesTo develop and internally validate prediction models to assess treatment success of both stand-alone and blended online vestibular rehabilitation (VR) in patients with chronic vestibular syndrome.DesignSecondary analysis of a randomised controlled trial.Setting59 general practices in The Netherlands.Participants202 adults, aged 50 years and older with a chronic vestibular syndrome who received either stand-alone VR (98) or blended VR (104). Stand-alone VR consisted of a 6-week, internet-based intervention with weekly online sessions and daily exercises. In blended VR, the same intervention was supplemented with physiotherapy support.Main outcome measuresSuccessful treatment was defined as: clinically relevant improvement of (1) vestibular symptoms (≥3 points improvement Vertigo Symptom Scale—Short Form); (2) vestibular-related disability (>11 points improvement Dizziness Handicap Inventory); and (3) both vestibular symptoms and vestibular-related disability. We assessed performance of the predictive models by applying calibration plots, Hosmer-Lemeshow statistics, area under the receiver operating characteristic curves (AUC) and applied internal validation.ResultsImprovement of vestibular symptoms, vestibular-related disability or both was seen in 121, 81 and 64 participants, respectively. We generated predictive models for each outcome, resulting in different predictors in the final models. Calibration for all models was adequate with non-significant Hosmer-Lemeshow statistics, but the discriminative ability of the final predictive models was poor (AUC 0.54 to 0.61). None of the identified models are therefore suitable for use in daily general practice to predict treatment success of online VR.ConclusionIt is difficult to predict treatment success of internet-based VR and it remains unclear who should be treated with stand-alone VR or blended VR. Because we were unable to develop a useful prediction model, the decision to offer stand-alone or blended VR should for now be based on availability, cost effectiveness and patient preference.Trial registration numberThe Netherlands Trial Register NTR5712.

scholarly journals Validation of prediction models of severe disease course and non-achievement of remission in juvenile idiopathic arthritis: part 1—results of the Canadian model in the Nordic cohort

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Veronika Rypdal ◽  
◽  
Jaime Guzman ◽  
Andrew Henrey ◽  
Thomas Loughin ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Prediction Model ◽  
Prediction Models ◽  
Severe Disease ◽  
Disease Onset ◽  
Fine Tuning ◽  
Disease Course ◽  
Internal Validation ◽  
Long Term Outcome

Abstract Background Models to predict disease course and long-term outcome based on clinical characteristics at disease onset may guide early treatment strategies in juvenile idiopathic arthritis (JIA). Before a prediction model can be recommended for use in clinical practice, it needs to be validated in a different cohort than the one used for building the model. The aim of the current study was to validate the predictive performance of the Canadian prediction model developed by Guzman et al. and the Nordic model derived from Rypdal et al. to predict severe disease course and non-achievement of remission in Nordic patients with JIA. Methods The Canadian and Nordic multivariable logistic regression models were evaluated in the Nordic JIA cohort for prediction of non-achievement of remission, and the data-driven outcome denoted severe disease course. A total of 440 patients in the Nordic cohort with a baseline visit and an 8-year visit were included. The Canadian prediction model was first externally validated exactly as published. Both the Nordic and Canadian models were subsequently evaluated with repeated fine-tuning of model coefficients in training sets and testing in disjoint validation sets. The predictive performances of the models were assessed with receiver operating characteristic curves and C-indices. A model with a C-index above 0.7 was considered useful for clinical prediction. Results The Canadian prediction model had excellent predictive ability and was comparable in performance to the Nordic model in predicting severe disease course in the Nordic JIA cohort. The Canadian model yielded a C-index of 0.85 (IQR 0.83–0.87) for prediction of severe disease course and a C-index of 0.66 (0.63–0.68) for prediction of non-achievement of remission when applied directly. The median C-indices after fine-tuning were 0.85 (0.80–0.89) and 0.69 (0.65–0.73), respectively. Internal validation of the Nordic model for prediction of severe disease course resulted in a median C-index of 0.90 (0.86–0.92). Conclusions External validation of the Canadian model and internal validation of the Nordic model with severe disease course as outcome confirm their predictive abilities. Our findings suggest that predicting long-term remission is more challenging than predicting severe disease course.

scholarly journals The Emergency Surgery Frailty Index (EmSFI): development and internal validation of a novel simple bedside risk score for elderly patients undergoing emergency surgery

2020 ◽  
Author(s):  
Gianluca Costa ◽  
◽  
Laura Bersigotti ◽  
Giulia Massa ◽  
Luca Lepre ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Surgical Procedures ◽  
Emergency Surgery ◽  
Characteristic Curve ◽  
Frailty Index ◽  
Univariate Analysis ◽  
The Elderly ◽  
Predictive Score ◽  
Internal Validation ◽  
Discrimination Ability

Abstract Background Frailty assessment has acquired an increasing importance in recent years and it has been demonstrated that this vulnerable profile predisposes elderly patients to a worse outcome after surgery. Therefore, it becomes paramount to perform an accurate stratification of surgical risk in elderly undergoing emergency surgery. Study design 1024 patients older than 65 years who required urgent surgical procedures were prospectively recruited from 38 Italian centers participating to the multicentric FRAILESEL (Frailty and Emergency Surgery in the Elderly) study, between December 2016 and May 2017. A univariate analysis was carried out, with the purpose of developing a frailty index in emergency surgery called “EmSFI”. Receiver operating characteristic curve analysis was then performed to test the accuracy of our predictive score. Results 784 elderly patients were consecutively enrolled, constituting the development set and results were validated considering further 240 consecutive patients undergoing colorectal surgical procedures. A logistic regression analysis was performed identifying different EmSFI risk classes. The model exhibited good accuracy as regard to mortality for both the development set (AUC = 0.731 [95% CI 0.654–0.772]; HL test χ2 = 6.780; p = 0.238) and the validation set (AUC = 0.762 [95% CI 0.682–0.842]; HL test χ2 = 7.238; p = 0.299). As concern morbidity, our model showed a moderate accuracy in the development group, whereas a poor discrimination ability was observed in the validation cohort. Conclusions The validated EmSFI represents a reliable and time-sparing tool, despite its discriminative value decreased regarding complications. Thus, further studies are needed to investigate specifically surgical settings, validating the EmSFI prognostic role in assessing the procedure-related morbidity risk.

Prognostic factors for cancer patients with good performance status considered for inclusion in phase I clinical trials

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 2568-2568
Author(s):  
M. Bonneterre ◽  
N. Penel ◽  
M. Vanseymortier ◽  
E. Dansin ◽  
S. Clisant ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Prognostic Factors ◽  
Serum Albumin ◽  
Phase I ◽  
Cancer Patients ◽  
Cox Model ◽  
Performance Status ◽  
Univariate Analysis ◽  
Rank Test ◽  
Phase I Clinical Trials

2568 Background: For investigators, the selection of patients to be considered for phase I clinical trials is difficult, because of the lack of objective criteria for a rational decision-making process. From October 1997 to October 2002, we retrospectively assessed prognostic factors for cancer patients considered for Phase 1 trials. Methods: 148 consecutive patients who had been screened for inclusion in 6 different phase I trials were included in the present study. 70 out of them actually received the phase I treatment. Univariate (Log-Rank test) and multivariate analysis (Cox proportional hazard ratio model) were performed to determine the prognostic factors related to overall survival (OS) after screening. Results: The study comprised 63 men and 85 women, with a median age of 54 (range 23–79). The most frequent primary cancer sites were: breast (38 cases), head and neck (28 cases), lung (18 cases) and colorectal (17 cases). 91 out of them had a performance status PS = 0. The median OS of the 148 patients was 5.7 months (173 days, range 1–2,421). Univariate analysis identified PS = 1, Body Mass Index < 20, liver and visceral metastasis, serum albumin < 38 g/L, lymphocytes count < 0.7 x 109/L and granulocytes count > 7.5 x 109/L as poor prognostic factors. The Cox model identified serum albumin < 38 g/L (HR 2.51 [1.51–4.18], p=0.0001) and lymphocyte count < 0.7 x 109/L (HR 2.27 [1.13–4.62], p=0.024) as independent prognostic variables for OS. All patients presenting with both prognostic factors died within 90 days. Conclusion: We propose a simple model, easily obtained at the patient bedside, which can discriminate patients who have a life expectancy of over 3 months and thus could be enrolled in phase-I anti-cancer trials. No significant financial relationships to disclose.

Outcome of colon cancer patients with synchronous metastases

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 4029-4029
Author(s):  
I. Sobhani ◽  
F. Roudot-Thoraval ◽  
F. Mesli ◽  
B. Landi ◽  
T. Aparicio ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Cox Model ◽  
Univariate Analysis ◽  
Colon Surgery ◽  
Teaching Hospitals ◽  
Rank Test ◽  
Metastatic Colon Cancer ◽  
Curative Surgery ◽  
Synchronous Metastases

4029 Background: Metastatic colon cancer patients may undergo chemotherapy without colon surgery. However, the outcome of patients has not been evaluated and antiagiogenic agents can not be given. The aim of the present cohort study was to analyse factors influencing patients’ survival. Methods: Consecutive patients [N=228, mean age (sd) 64 (12) yrs, median follow-up 20 mths;84 females] treated in 6 teaching hospitals received chemotherapy for metastatic colonic cancer, either as the first step, or after surgery. Progressive free survival (PFS) was estimated using Kaplan-Meïer method. Factors associated with PFS were tested by means of Log rank test and results are presented in terms of medians of survival (95% CI). Factors independently related to PFS were tested using a Cox model and results are presented as hazard ratio. Results: 105 patients with colon cancer and synchronous metastatsis underwent colon surgery prior to chemotherapy (68 males, mean age 64 yrs) when 123 patients were treated first by chemotherapy ± biotherapy (76 males, mean age 63 yrs). By univariate analysis, following factors were significantly associated with PFS: surgery first 25.5 (18.6 - 32.5) vs chemotherapy first 18.3 (14.7 - 21.9) mths p = 0.006; curative surgery: yes 35.7 (29.6 - 41.8) vs no 18.4 (15.6 - 21.2) mths p < 0.001; tumour histological differentiation : no : 13.4 (6.2 - 20.6) vs well : 24.7 (20.4 - 29.1) mths p<0.001; synchronous metastases: liver only 25.5 (20.5 - 30.6) vs peritonea&nodes : 18.4 (10.6 - 26.1) vs pulmonary & other sites : 16.5 (14.7 - 18.3) mths p < 0.0001; need for colonic stent: yes 16.4 (9.3 - 23.5) vs no 23.9 (21.1 - 26.7) months p < 0.0001; antiangiogenic drug: yes 36.6 (28.7 - 44.5) vs no : 20.7 (18.3 - 23.1) p = 0.033. After Cox multivariate analysis five independent factors were found to be associated with PFS. Conclusions: Colon surgery before chemotherapy plus bevacizumab appears to be the more appropriate choice, and associated with longer PFS, especially for those patients with well differentiated tumours and synchronous liver metastases. [Table: see text] No significant financial relationships to disclose.

Factors influencing survival in inflammatory breast cancer.

2014 ◽  
Vol 32 (26_suppl) ◽  
pp. 136-136
Author(s):  
Julie A. Cupp ◽  
Diane Liu ◽  
Yu Shen ◽  
Naoto T. Ueno ◽  
Ricardo H. Alvarez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Inflammatory Breast Cancer ◽  
Poor Prognosis ◽  
Cox Model ◽  
Univariate Analysis ◽  
Progression Free Survival ◽  
Rank Test ◽  
Rapid Progression ◽  
Progression Of Disease ◽  
Delay In Treatment

136 Background: Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer associated with poor prognosis, characterized by rapidly growing mass, skin changes, and regional adenopathy. The objective of this study was to determine if delay in treatment influenced survival in IBC patients. Methods: A prospective IBC database identified 93 women with stage III IBC who received care at MD Anderson from 2007 - 2012 and were retrospectively reviewed. All patients received neoadjuvant chemotherapy followed by surgery, unless progression of disease was noted, and postmastectomy radiation. Impact of time from onset of symptoms to chemotherapy or to surgery on overall survival (OS) and progression free survival (PFS) were evaluated after adjusting for the baseline covariates in the Cox model. Results: A majority of patients were white (77.4%) with an average age of 54 years. Average days from onset of symptoms to first chemo is 95 (range 16 – 387) and to surgery is 283 (range 184 – 585). Four patients had progression while on chemo. There were 14 deaths with median follow up of 2.6 years from diagnosis. In univariate analysis, delay in treatment, > 90 days from onset of symptoms to chemo, did not affect OS or PFS. Obtaining negative margins was statistically significant for OS and PFS measured from first chemo (p=0.005 and p=0.007). Positive HER-2 status was associated with longer PFS time from chemo (p=0.02, log-rank test) and from surgery (p=0.009). Positive progesterone receptor (PR) was found to be statistically significantly associated with longer OS time from chemo (p=0.01) and from surgery (p=0.03). Clinical and imaging response to chemo were associated with better OS (p=0.007 and p=0.005) and pathologic response was marginally associated with improved OS and PFS (p=0.07 and p=0.06), both measured from surgery. In multivariate Cox model, adjusting for PR or HER2, days from onset of symptoms to chemo or surgery did not have significant impact on OS or PFS. Conclusions: While traditionally delay diagnosis and treatment is considered one of the factors associated with poor prognosis, our study suggests otherwise. However, due to such rapid progression of disease, early diagnosis is still important in the overall management of patients diagnosed with IBC.

scholarly journals Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery

2022 ◽  
Vol 8 ◽  
Author(s):  
Jinzhang Li ◽  
Ming Gong ◽  
Yashutosh Joshi ◽  
Lizhong Sun ◽  
Lianjun Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Failure ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Scoring Systems ◽  
Acute Aortic Syndrome ◽  
Internal Validation ◽  
Medical Centers ◽  
Better Than

BackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation.ResultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p &lt; 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.ConclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.

Immunohistochemical (IHC) sub-classification of 105 triple negative breast cancers (TNBC).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12577-e12577
Author(s):  
Marion Stacoffe ◽  
Armelle Vinceneux ◽  
Flavie Arbion ◽  
Helene Vegas ◽  
Gaelle Fromont ◽  
...  
Keyword(s):  
Survival Data ◽  
Triple Negative ◽  
Cox Model ◽  
Univariate Analysis ◽  
Disease Free Survival ◽  
Rank Test ◽  
Mixed Group ◽  
Triple Negative Phenotype ◽  
Negative Phenotype

e12577 Background: Molecular data have shown that TNBC was a heterogeneous group of tumors. The objective was to evaluate prognosis of IHC sub-classification adapted from molecular model. Methods: We used IHC sub-classification based on positivity for androgen receptor (AR) (Roche, SP 107), cytokeratine 5/6 (CK) (Dako, D5/16B4) and Epidermal growth factor receptor (EGFR) (Biosd, 31G7). Samples with more than 10% AR nuclear immunostaining were considered positive. Threshold for CK and EGFR was 1%. We distinguished 4 groups of tumors: AR phenotype (AR+, EGFR-, CK5/6-), basal-like phenotype (AR-, EGFR+/-, CK5/6 +/-), triple-negative phenotype (AR-, EGFR-, CK5/6-) and mixed group (AR+, EGFR+/-, CK5/6 +/-). Tissue micro-array blocks were constructed with samples from a retrospective cohort treated in adjuvant setting for non metastatic TNBC in a single institution from 2003 to 2013. Survival data were estimated by the Kaplan-Meier method and compared by the log-rank test in univariate analysis. Multivariate analysis including tumor size (T), lymph nodes status (N) and lymphovascular invasion (LVI) was performed using Cox model. Results: 105 patients were followed-up for a median period of 56.3 months [6-155]. Median age was 54 years [29-80]. 57.1% were stage pT1, 41.9% were pN+ and 37,1% presented LVI. 11 patients were classified as AR phenotype, 35 as basal-like, 46 as triple-negative phenotype and 13 as mixed group. 18 patients developed metastases: 3/11 AR, 5/35 basal-like, 6/46 triple-negative and 4/13 mixed group. No difference was observed between TNBC subgroups in terms of disease free survival DFS (p = .98) and overall survival OS (p = .86). T, N and LVI were the only prognostic factors (p = .05, p = .05 and p = .046 respectively). Conclusions: We found no impact of IHC sub-classification of TNBC. Correlation between IHC and molecular biology is in progress.

Sign in / Sign up

Export Citation Format

Share Document