Collagenous Myofibroblastic Tumor of the Mandible: Case Report of a Unique Locally Aggressive Neoplasm Journal Head and Neck Pathology

We report a locally aggressive collagenous myofibroblastic neoplasm of the mandible in an 18-year-old male. Clinically, the lesion presented with rapid growth and irregular mandibular bone destruction. Grossly, the tumor was 10 cm in greatest dimension, light-tan, firm, and involving the posterior one-thirds of the body and inferior half of the left mandibular ramus. Histologically, the lesion was composed of a loose spindle cell proliferation interspersed with periodic dense bands of collagen. The spindle cells reacted positively to smooth muscle actin, calponin, and focally to desmin and were negative for S-100, pan-cytokeratin, CD99, CD34 and caldesmon, supporting myofibroblastic derivation. At our 4 year follow-up, the patient remained free of local recurrence and surgery related complications. The clinicopathologic findings and the differential diagnosis of this lesion is presented and discussed.

Chondrosarcoma of the mobile spine and sacrum.

Chondrosarcoma is a rare malignant tumor of bone. This family of tumors can be primary malignant tumors or a secondary malignant transformation of an underlying benign cartilage tumor. Pain is often the initial presenting complaint when chondrosarcoma involves the spine. In the mobile spine, chondrosarcoma commonly presents within the vertebral body and shows a predilection for the thoracic spine. Due to the resistance of chondrosarcoma to both radiation and chemotherapy, treatment is focused on surgery. With en bloc excision of chondrosarcoma of the mobile spine and sacrum patients can have local recurrence rates as low as 20%.

Prognostic factors in desmoid tumors

Purpose. To evaluate the prognostic factors in desmoid tumors in the light of its possible use in standardizing the treatment strategy of an individual patient. ^ Patients and methods. A retrospective review of 189 consecutive patients who were treated at MD Anderson Cancer Center (MDACC) from January 1995 to December 2005 was done. Univariate and multivariate analysis of different prognostic factors was done on all patients, patients treated with surgery alone, subset of patients who came to MDACC with primary tumor. The median follow up was 63 months. Also the analysis of 189 desmoid patients treated at MDACC between 1995 and 2005 was compared to results of 189 desmoid patients treated at MDACC from 1965-1994 using data retrieved from a 150 field prospective relational soft tissue tumor database. ^ Results. 5-, and 10-year overall survival rate were 0.976 (95%CI 0.952, 0.999), and 0.966 (95% CI 0.935, 0.996), respectively. 5-, and 10-year recurrence free rate were 0.803 (95%CI 0.738, 0.868), and 0.793 (95% CI 0.726, 0.860), respectively. 5 year recurrence free survival for surgery alone, radiotherapy alone, chemotherapy alone and combination regimen were 0.759, 0.625, 0.933, and 0.802 respectively. Age (>30 vs. <=30) and primary tumor site (extremity vs visceral) were two prognostic factors significantly associated with local recurrence in all of the patients. ^ Conclusion. An increased awareness of the complex multidisciplinary management needed for successful control of desmoid tumor may underlie a significantly increased number of desmoid referrals, especially primary untreated desmoids, to UTMDACC. The careful prospective integration of multiple therapies has led to a significant recent improvement in desmoid patient outcome. These trends should be supported, particularly if personalized molecular-based therapies are to be rapidly and effectively deployed for the benefit of those afflicted by this rare and potentially devastating disease.^

A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^