Purpose: This phase 1 study evaluated the pharmacokinetic and pharmacodynamic effects of cetuximab on patients with epithelial malignancies. Experimental Design: Following a skin and tumor biopsy, patients with advanced epithelial malignancies were randomized to receive a single dose of cetuximab at 50, 100, 250, 400, or 500 mg/m 2 i.v. Repeat skin (days 2, 8, 15, and 22) and tumor (day 8) biopsies were obtained. Immunohistochemical expression of epidermal growth factor receptor (EGFR) and its pathway members was done on biopsies. Blood samples were obtained over 22 days for pharmacokinetic analyses. After day 22, all patients received weekly 250 mg/m 2 cetuximab until disease progression or unacceptable toxicity. Results: Thirty-nine patients enrolled. Rash was noted in 26 (67%) patients.Three patients (two with colon cancer and one with laryngeal cancer) achieved a partial response and 13 patients had stable disease. Pharmacokinetic data revealed mean maximum observed cetuximab concentrations and mean area under the concentration-time curve from time zero to infinity increased in a dose-dependent manner up to 400 mg/m 2 cetuximab. Mean clearance was similar at cetuximab doses z100 mg/m 2 , supporting saturation of EGFR binding at 250 mg/m 2 . Pharmacodynamic evaluation revealed that patients with partial response/stable disease had a higher-grade rash and higher cetuximab trough levels than those with progressive disease (P = 0.032 and 0.002, respectively). Administration of single doses (250-500 mg/m 2 ) of cetuximab resulted in a dosedependent decrease in EGFR protein expression levels in skin over time, supporting a minimal dose of cetuximab at 250 mg/m 2 for a pharmacodynamic effect. Conclusion: This study provides a pharmacokinetic and pharmacodynamic rationale for the dosing of cetuximab.
An adequate reproducibility in the description of tissue architecture is still a challenge to diagnostic pathology, sometimes with unfortunate prognostic implications. To assess a possible diagnostic and prognostic value of quantitiative tissue architecture analysis, structural features based on the Voronoi Diagram (VD) and its subgraphs were developed and tested.A series of 27 structural features were developed and tested in a pilot study of 30 cases of prostate cancer, 10 cases of cervical carcinomas, 8 cases of tongue cancer and 8 cases of normal oral mucosa. Grey level images were acquired from hematoxyline‐eosine (HE) stained sections by a charge coupled device (CCD) camera mounted on a microscope connected to a personal computer (PC) with an image array processor. From the grey level images obtained, cell nuclei were automatically segmented and the geometrical centres of cell nuclei were computed. The resulting 2‐dimensional (2D) swarm of pointlike seeds distributed in a flat plane was the basis for construction of the VD and its subgraphs. From the polygons, triangulations and arborizations thus obtained, 27 structural features were computed as numerical values. Comparison of groups (normal vs. cancerous oral mucosa, cervical and prostate carcinomas with good and poor prognosis) with regard to distribution in the values of the structural features was performed with Student's t‐test.We demonstrate that some of the structural features developed are able to distinguish structurally between normal and cancerous oral mucosa (P=0.001), and between good and poor outcome groups in prostatic (P=0.001) and cervical carcinomas (P=0.001).We present results confirming previous findings that graph theory based algorithms are useful tools for describing tis‐ sue architecture (e.g., normal versus malignant). The present study also indicates that these methods have a potential for prognostication in malignant epithelial lesions.
BackgroundAutomation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies.MethodsDefined monomicrobial and clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and clinical samples.ResultsThe detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and clinical urine samples, despite the presence of multiple species in the clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested.ConclusionThe development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories.
SUMMARY:Several studies on oral squamous cell carcinomas (OSCC) suggest that the clinical value of traditional histologic grading is limited both by poor reproducibility and by low prognostic impact. However, the prognostic potential of a strictly quantitative and highly reproducible assessment of the tissue architecture in OSCC has not been evaluated. Using image analysis, in 193 cases of T1-2 (Stage I-II) OSCC we retrospectively investigated the prognostic impact of two graph theory-derived structural features: the average Delaunay Edge Length (DEL_av) and the average homogeneity of the Ulam Tree (ELH_av). Both structural features were derived from subgraphs of the Voronoi Diagram. The geometric centers of the cell nuclei were computed, generating a two-dimensional swarm of point-like seeds from which graphs could be constructed. The impact on survival of the computed values of ELH_av and DEL_av was estimated by the method of Kaplan and Meier, with relapse-free survival and overall survival as end-points. The prognostic values of DEL_av and ELH_av as computed for the invasive front, the superficial part of the carcinoma, the total carcinoma, and the normal-appearing oral mucosa were compared. For DEL_av, significant prognostic information was found in the invasive front (p Ͻ 0.001). No significant prognostic information was found in superficial part of the carcinoma (p ϭ 0.34), in the carcinoma as a whole (p ϭ 0.35), or in the normal-appearing mucosa (p ϭ 0.27). For ELH_av, significant prognostic information was found in the invasive front (p ϭ 0.01) and, surprisingly, in putatively normal mucosa (p ϭ 0.03). No significant prognostic information was found in superficial parts of the carcinoma (p ϭ 0.34) or in the total carcinoma (p ϭ 0.11). In conclusion, strictly quantitative assessment of tissue architecture in the invasive front of OSCC yields highly prognostic information.
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