scholarly journals Prognostic significance of viable tumor size measurement in hepatocellular carcinomas after preoperative locoregional treatment

2021 ◽  
Author(s):  
Yoon Jung Hwang ◽  
Youngeun Lee ◽  
Hyunjin Park ◽  
Yangkyu Lee ◽  
Kyoungbun Lee ◽  
...  
Keyword(s):  
Tumor Size ◽  
Prognostic Significance ◽  
Size Measurement ◽  
Locoregional Treatment ◽  
Hepatocellular Carcinomas ◽  
Viable Tumor

Histopathologic Features of Prognostic Significance in High-Grade Osteosarcoma

2016 ◽  
Vol 140 (11) ◽  
pp. 1231-1242 ◽  
Author(s):  
Michael Herman Chui ◽  
Rita A. Kandel ◽  
Marcus Wong ◽  
Anthony M. Griffin ◽  
Robert S. Bell ◽  
...  
Keyword(s):  
Overall Survival ◽  
Tumor Size ◽  
Primary Tumor ◽  
Lymphovascular Invasion ◽  
Prognostic Significance ◽  
High Grade ◽  
Histologic Subtype ◽  
Primary Tumor Size ◽  
Viable Tumor ◽  
High Grade Osteosarcoma

Context.— In osteosarcoma treated with neoadjuvant chemotherapy the extent of tumor necrosis on resection is considered an indicator of treatment response, and this has been shown to correlate with survival in most but not all studies. Objective.— To identify additional histologic variables of prognostic significance in high-grade osteosarcoma. Design.— Slides of pretreatment biopsy and primary postneoadjuvant chemotherapy resections from 165 patients with high-grade osteosarcoma were reviewed. Univariate (Kaplan-Meier) and multivariate (Cox regression) analyses were performed to identify clinical and histomorphologic attributes associated with overall survival. Results.— Univariate analyses confirmed the prognostic significance of metastatic status on presentation, primary tumor size, anatomic site, and histologic subtype. Additionally, the identification of lymphovascular invasion, 10% or more residual viable tumor, and 10 or more mitoses per 10 high-powered fields assessed in posttreatment resections were associated with poor survival, retaining significance in multivariate analyses. Based on results from multivariate analysis, we developed a prognostic index incorporating primary tumor size and site, and significant histologic features assessed on resection (ie, lymphovascular invasion status, mitotic rate, and extent of viable tumor). This scoring system segregates patients into 3 risk categories with significant differences in overall survival and retained significance in an independent validation set of 42 cases. Conclusions.— The integration of clinical and microscopic features improves prognostication of patients with osteosarcoma.

scholarly journals Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

2009 ◽  
Vol 27 (8) ◽  
pp. 1160-1167 ◽  
Author(s):  
Joel S. Parker ◽  
Michael Mullins ◽  
Maggie C.U. Cheang ◽  
Samuel Leung ◽  
David Voduc ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Risk Score ◽  
Tumor Size ◽  
Pathologic Complete Response ◽  
Prognostic Significance ◽  
Intrinsic Subtype ◽  
Cancer Prognosis ◽  
Intrinsic Subtypes ◽  
Node Negative

Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.

scholarly journals Machine Learning-Based Prediction Model for Papillary Thyroid Carcinoma Recurrence

2020 ◽  
Author(s):  
Young Min Park ◽  
Byung-Joo Lee
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Tumor Size ◽  
Large Scale ◽  
Prediction Models ◽  
Prognostic Significance ◽  
Disease Recurrence ◽  
Machine Learning Techniques ◽  
Papillary Thyroid ◽  
Recurrence Prediction

Abstract Background: This study analyzed the prognostic significance of nodal factors, including the number of metastatic LNs and LNR, in patients with PTC, and attempted to construct a disease recurrence prediction model using machine learning techniques.Methods: We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with papillary thyroid cancer between 2003 and 2009. Results: We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Conclusions: We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. Large-scale multicenter clinical studies should be performed to improve the performance of our prediction models and verify their clinical effectiveness.

P31 Prognostic significance of vascular tumor emboli in 1518 breast carcinoma patients with small tumor size (≤3 cm)

1998 ◽  
Vol 34 ◽  
pp. S21
Author(s):  
J.-Y. Pierga ◽  
A. Vincent-Salomon ◽  
M. Cousineau ◽  
B. Zafrani ◽  
B. Asselain ◽  
...  
Keyword(s):  
Breast Carcinoma ◽  
Tumor Size ◽  
Prognostic Significance ◽  
Vascular Tumor ◽  
Small Tumor ◽  
Tumor Emboli ◽  
Small Tumor Size

The prognostic signficance of pretreatment leukocytosis in patients with anal cancer treated with radical chemoradiotherapy or radiotherapy.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 656-656 ◽  
Author(s):  
Robyn Banerjee ◽  
George Roxin ◽  
Misha Eliasziw ◽  
Kurian Joseph ◽  
Donald Buie ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Patient Group ◽  
Anal Cancer ◽  
Tumor Size ◽  
Prognostic Significance ◽  
Disease Free Survival ◽  
Cox Proportional Hazards ◽  
Prognostic Impact ◽  
Pre Treatment ◽  
Poor Outcomes

656 Background: There are emerging data showing prognostic significance of pre-treatment leukocytosis (elevated white blood cell count) in cervical cancer patients. However the prognostic impact of leukocytosis in anal cancer patients has not been previously reported. The purpose of this study was to determine the association of pre-treatment leukocytosis on outcome in patients with anal cancer treated with radical chemoradiotherapy (CRT) or radiotherapy (RT). Methods: 126 patients with anal cancer, treated with radical CRT (91.3%) or RT (8.7%) from 2 major Canadian cancer centers (University of Calgary, n=65 and University of Alberta, n=61), between 2000 and 2008 were evaluated. Demographic, clinical, hematologic and treatment factors were retrieved from retrospective review of the patients’ records. The association of clinical factors and hematologic status with overall survival (OS) and disease-free survival (DFS) was analyzed using Cox proportional hazards regression models. Results: Median follow-up was 24 months. Median tumor size was 4 cm. Mean age was 59 years and M:F was 29:97. Pre-treatment leukocytosis (WBC count greater than 10^9/L) was identified in 16% (20/126) of patients. After adjusting for gender, tumor size and stage in a multivariate analysis, leukocytosis remained significantly associated with worse 2-year OS [HR 2.9 (95% CI 1.1-7.9), p=0.036] and worse DFS [HR 2.2 (95% CI1.1-4.8), p=.045]. The patient group with both pre-treatment hemoglobin (Hgb) less than 125 g/L (lowest quartile) and leukocytosis had very poor outcomes, 2-year OS 61% versus 89% for patients without these factors; more than doubling the hazard for DFS [HR2.7 (95% CI 1.1-6.8), p=0.033] and for OS [4.5 (95% CI 1.5-13.2), p=.006]. Conclusions: Pre-treatment leukocytosis is associated with worse OS and DFS in patients with anal cancer treated with radical CRT or RT. Patients with both low Hgb and leukocytosis had very poor outcomes. These hematologic parameters represent potential biomarkers for prognosis and treatment response, and warrant further investigation to uncover the underlying biologic mechanisms and therapeutic strategies in this patient group.

Prediction of a potential lethal phenotype in testicular nonseminomatous germ cell tumor.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e15575-e15575
Author(s):  
Mehmet Asim Bilen ◽  
Sue-Hwa Lin ◽  
Rosale General ◽  
Lance C. Pagliaro ◽  
Christopher Logothetis ◽  
...  
Keyword(s):  
Germ Cell ◽  
Germ Cell Tumor ◽  
Tumor Size ◽  
Primary Tumor ◽  
Embryonal Carcinoma ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Cell Tumor ◽  
Lethal Phenotype ◽  
Nonseminomatous Germ Cell Tumor

e15575 Background: Cancer is a heterogeneous disease. The nature of heterogeneity provides clues about the biologic origin of cancer. Studying cancer heterogeneity may enable us to elucidate the various subtypes of a particular cancer, and improve the diagnosis, prognostication, and therapy of cancer. Nonseminomatous germ cell tumor (NSGCT) is a prime example of a heterogeneous disease. We determined the lethality of NSGCT by characterizing the histological makeup of primary testicular tumors (proportion of embryonal carcinoma [EC], choriocarcinoma, seminoma, yolk sac tumor [YST], and teratoma) and their tumor burden. Methods: From November 1997 to October 2012, 142 consecutive patients diagnosed with a NSGCT were included for this study. Predictors significant in univariate analysis: tumor stage, tumor size, percentage of YST component, and percentage of EC component were used as predictors of cancer specific death or incurability in a multivariate binary logistic regression model. The outcome of the model is probability of death or incurability. Results: Patient characteristics: age, median (range) – 25 (12-53); race – Caucasians 65%, Hispanics 28%; pathology – embryonal carcinoma 15%, teratoma 6%, mixed NSGCT 79%; stage – I 43%, II 33%, III 24%; size of primary tumor, median (range) – 3.8 cm (0.9-27); HCG<5,000 95%, >5,000 4%; AFP<1,000 85%, >1,000 13%; therapy – salvage chemotherapy 15%, RPLND 34%, HD chemotherapy w/ SCT 1%, WBRT 1%. Univariate analysis showed that clinical stage (p=.0001), presence of YST (p<.005), size of primary tumor (p=.005), and absence of EC (<.01) have prognostic significance. We developed a nomogram to identify patients who are most likely to succumb to their NSGCT based on the histologic makeup of their primary tumor, size of the primary tumor, initial tumor markers, and appropriate surgeries after chemotherapy. Conclusions: Presence of YST in advanced NSGCT predicts chemotherapy resistance in a potentially lethal phenotype. Early diagnosis and timely treatment with chemotherapy and surgery may improve the clinical outcome of these patients. Results of this study need to be validated in another data base or a prospective clinical trial.

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