In this study, we explore a mathematical model to characterize the clustered microcalcifications on mammograms for predicting the pathological classification and grading. Our database consists of both retrospective cases (78 cases) and prospective cases (31 cases) with pathologically diagnosed clusters of microcalcifications on mammograms. The microcalcifications were divided into four grades: grade 0, benign breast disease including mastopathies (n = 12) and fibroadenomas (n = 20); grade 1, well-differentiated infiltrating ductal carcinoma (n = 12); grade 2, moderately differentiated infiltrating ductal carcinoma (n = 38); grade 3, poorly differentiated infiltrating ductal carcinoma (n = 27). A feature parameter, defined as the pattern form factor of microcalcification cluster θ by us, combines five computer-extracted image parameters of microcalcification clusters of those mammograms. In every case, only one imaging was selected for modeling analysis. A total of 109 imagings were adopted in current study. We find the existence of a positive relationship between the feature parameter θ and pathological grading G of microcalcifications in retrospective cases, which was expressed as G =  6.438 + 1.186 ×  Ln <θ>. The model above has been verified further by the prospective study with a comparative evaluation accuracy of approximately 77.42%. The binary predication simply for both benignancy and malignancy was also included using same but reshuffled data, and the receiver operating characteristic (ROC) analysis was performed with ROC value 0.74351∼0.79891. As one candidate for feature parameter in computer-aided diagnosis, the pattern form factor θ of clustered microcalcifications may be useful to predict the pathological grading and classification of microcalcification clusters on mammography in breast cancer.

Background

Intensity-modulated radiation therapy (IMRT) provides excellent locoregional control for nasopharyngeal carcinoma (NPC), and has gradually replaced two-dimensional conventional radiotherapy as the first-line radiotherapy technique. Furthermore, in the new seventh edition of the American Joint Committee on Cancer (AJCC) staging system, retropharyngeal lymph nodes were upgraded from N0 to N1 disease as a result of their negative impact on the distant metastasis-free survival (DMFS) rates of NPC. This retrospective study was conducted in order to review the treatment outcomes and patterns of failure in NPC patients with N0 disease after IMRT in order to effectively guide treatment in the future.

Methods

We retrospectively reviewed data from 506 biopsy-proven nonmetastatic NPC patients. There were 191 patients with negative cervical lymph node involvement. According to the seventh edition of the American Joint Committee on Cancer (AJCC) staging system, 110 patients (21.7%) were staged with N0 disease, and 81 patients (16.0%) were reclassified with N1 disease due to the presence of RLN metastasis. All patients received IMRT as the primary treatment.

Results

In patients with negative cervical lymph node involvement, distant metastasis-free survival (DMFS) was significantly higher in patients without retropharyngeal lymph node (RLN) metastasis than those with RLN metastasis (95.9% vs. 88.1% respectively, P = 0.04). For N0 disease, the 5-year overall survival (OS), local relapse-free survival (LRFS), nodal relapse-free survival (NRFS) and DMFS rates were 93.8%, 97.1%, 99.1% and 95.9%, respectively. For T1N0, T2N0, T3N0 and T4N0, OS was 97.8%, 100%, 93.8% and 76.9%, LRFS was 100%, 92.9%, 100% and 88.9% and DMFS was 96.6%, 90.9%, 100% and 93.3%, respectively. OS and LRFS were higher in T1-3 N0 patients than T4N0 patients (P < 0.01 and P = 0.01, respectively).

Conclusions

The seventh edition of the AJCC N-staging system improves prognostic accuracy by upgrading RLN metastasis to N1 disease. IMRT produces excellent survival rates in T1-3 N0 disease; however, T4N0 disease remains a challenge and additional improvements are required to achieve a favorable prognosis for these NPC patients.

Clustering periodically expressed genes from their time-course expression data could help understand the molecular mechanism of those biological processes. In this paper, we propose a nonlinear model-based clustering method for periodically expressed gene profiles. As periodically expressed genes are associated with periodic biological processes, the proposed method naturally assumes that a periodically expressed gene dataset is generated by a number of periodical processes. Each periodical process is modelled by a linear combination of trigonometric sine and cosine functions in time plus a Gaussian noise term. A two stage method is proposed to estimate the model parameter, and a relocation-iteration algorithm is employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. One synthetic dataset and two biological datasets were employed to evaluate the performance of the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g., k-means) for periodically expressed gene data, and thus it is an effective cluster analysis method for periodically expressed gene data.

Background

Metastasis-associated protein 1 (MTA1) has been associated with poor prognosis in several malignant carcinomas. The purpose of this study was to investigate the expression and prognostic value of MTA1 in nasopharyngeal carcinoma (NPC).

Methods

MTA1 expression was assessed using immunohistochemistry in paraffin-embedded tumor specimens from 208 untreated NPC patients. Cox regression analysis was used to calculate the hazard ratio (HR), 95% confidence interval (CI) and identify independent prognostic factors, and recursive partitioning analysis was used to create a decision tree.

Results

Nuclear overexpression of MTA1 was observed in 48.6% (101/208) of the NPC tissues. Nuclear overexpression of MTA1 correlated positively with N classification (P = 0.02), clinical stage (P = 0.04), distant metastasis (P < 0.01) and death (P = 0.01). Additionally, nuclear overexpression of MTA1 correlated significantly with poorer distant metastasis-free survival (DMFS; P <0.01) and poorer overall survival (OS; P < 0.01). MTA1 had prognostic significance in NPC patients with stage II disease, but not stage III or IV disease. Multivariate analysis demonstrated that nuclear overexpression of MTA1 was independently associated with poorer DMFS (HR, 2.05; 95% CI, 1.13–3.72; P = 0.02) and poorer OS (HR, 1.98; 95% CI, 1.09–3.59; P = 0.03). Using recursive partitioning analysis, the NPC patients could be classified with a low, intermediate or high risk of distant metastasis and death, on the basis of clinical stage, age and MTA1 expression.

Conclusion

The results of this study suggest that nuclear overexpression of MTA1 correlates significantly with poorer DMFS and poorer OS in NPC. MTA1 has potential as a novel prognostic biomarker in NPC.