Evaluation of semiautomated measurements of mesothelioma tumor thickness on CT scans

RATIONALE AND OBJECTIVES: To evaluate the clinical acceptability of semiautomated methods for the measurement of mesothelioma tumor thickness in computed tomography (CT) scans. MATERIALS AND METHODS: A computer interface was developed to allow the acquisition of semiautomated mesothelioma tumor thickness measurements, which require the manual selection of a point along the outer margin of the tumor in a CT section. After application of an automated lung segmentation method, the computer automatically identifies a corresponding point along the inner margin of the tumor (as represented by the lung boundary), constructs a line segment between the manually selected outer tumor margin point and the computer-determined inner tumor margin point, and computes tumor thickness as the length of this line segment. Three radiologists and oncologists independently reviewed line segments representing the semiautomated measurements generated by three different algorithms at 134 measurement sites in the CT scans of 22 mesothelioma patients. The observers either accepted a measurement line segment or modified it through the interface. Differences between the initial semiautomated measurements and the measurements as modified by the observers were analyzed. RESULTS: The frequency with which observers accepted the semiautomated measurements without modification was as high as 86%. Of all measurements across all observers and methods (1,206 measurements), 89% were changed by 2 mm or less. CONCLUSION: We have developed semiautomated methods to measure mesothelioma tumor thickness. The potential of these methods has been demonstrated through an observer study. We expect these methods to become important tools for the efficient quantification of tumor extent.     

Multispectral analysis of magnetic resonance images

Magnetic resonance (MR) imaging systems produce spatial distribution estimates of proton density, relaxation time, and flow, in a two dimensional matrix form that is analogous to that of the image data obtained from multispectral imaging satellites. Advanced NASA satellite image processing offers sophisticated multispectral analysis of MR images. Spin echo and inversion recovery pulse sequence images were entered in a digital format compatible with satellite images and accurately registered pixel by pixel. Signatures of each tissue class were automatically determined using both supervised and unsupervised classification. Overall tissue classification was obtained in the form of a theme map. In MR images of the brain, for example, the classes included CSF, gray matter, white matter, subcutaneous fat, muscle, and bone. These methods provide an efficient means of identifying subtle relationships in a multi-image MR study.     

A medical oncologist’s approach to immunotherapy for advanced renal tumors: Is nephrectomy indicated?

Metastatic renal cell carcinoma is highly resistant to systemic therapy. Although interleukin-2 and interferon remain the most active agents for this disease, long-term survival rates remain poor. Two phase 3 trials, European Organization Research and Treatment of Cancer 30947 and Southwest Oncology Group 8949, have demonstrated a survival benefit of nephrectomy followed by interferon versus interferon alone in patients having an excellent performance status (PS 0 and 1). Removal of the primary tumor followed by interferon is not recommended for patients with a moderate or poor PS (PS 2–4). Even with this aggressive approach, most patients eventually will die from their kidney cancer; therefore, every patient with metastatic disease should be considered for enrollment into clinical trials.     

Gene expression profiling of renal medullary carcinoma: potential clinical relevance

BACKGROUND: Renal medullary carcinoma is a rare kidney tumor with highly aggressive behavior. This tumor occurs exclusively in young patients with sickle cell trait or disease. To the authors' knowledge, very little is known to date regarding the underlying molecular genetics of this tumor, and no effective therapy has been established. METHODS: The authors analyzed the gene expression profiles of 2 renal medullary carcinomas from patients with sickle cell trait using microarrays containing 21,632 cyclic DNA (cDNA) clones and compared them with the gene expression profiles of 64 renal tumors. RESULTS: Based on global gene clustering with 3583 selected cDNAs, the authors found a distinct molecular signature of renal medullary carcinoma, which clustered closely with urothelial (transitional cell) carcinoma of the renal pelvis, rather than renal cell carcinoma (RCC). This finding of a significant difference in the gene expression patterns of renal medullary carcinoma compared with RCC suggests that this tumor should not be treated as a conventional RCC but, rather, as a special malignancy. This study also identified genes/proteins that may serve as biomarkers for renal medullary carcinoma or as potential targets of novel therapies. In addition, comparative genomic microarray analysis allowed the authors to predict the lack of chromosomal imbalances in this tumor. CONCLUSIONS: To the authors' knowledge, the current study is the first molecular profiling of renal medullary carcinoma, a rare but highly aggressive kidney carcinoma. The genes that are expressed specifically in this tumor may lead to not only a better understanding of its molecular pathways and discoveries of novel diagnostic markers but also, more important, to effective therapeutic interventions.