PURPOSE To test the hypothesis that increased pelvic bone marrow (BM) irradiation is associated with increased hematologic toxicity (HT) in cervical cancer patients undergoing chemoradiotherapy (CRT), and to develop a normal tissue complication probability (NTCP) model for HT. METHODS AND MATERIALS We tested associations between hematologic nadirs during CRT and the volume of BM receiving ≥ 10 and 20 Gy (V10 and V20) using a previously developed linear regression model. The validation cohort consisted of 44 cervical cancer patients treated with concurrent cisplatin and pelvic radiotherapy. Subsequently, these data were pooled with 37 identically treated patients from a prior study, forming a cohort of 81 patients for NTCP analysis. Generalized linear modeling was used to test associations between hematologic nadirs and dosimetric parameters, adjusting for body mass index. Receiver operating characteristic curves were used to derive optimal dosimetric planning constraints. RESULTS In the validation cohort, significant negative correlations were observed between white blood cell count (WBC) nadir and V10 (regression coefficient (β)=−0.060, p=0.009) and V20 (β=−0.044, p=0.010). In the combined cohort, the (adjusted) β estimates for log(WBC) vs. V10 and V20 were: −0.022 (p=0.025) and −0.021 (p=0.002), respectively. Patients with V10 ≥ 95% were more likely to experience grade ≥ 3 leukopenia (68.8% vs. 24.6%, p<0.001) as were patients with V20 > 76% (57.7% vs. 21.8%, p=0.001). CONCLUSIONS These findings support the hypothesis that HT increases with increasing pelvic BM volume irradiated. Efforts to maintain V10 < 95% and V20 < 76% may reduce HT.

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The results from this study suggest that RapidArc VMAT technique is dosimetrically accurate, safe, and efficient in delivering TMI within clinically acceptable time frame.

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In this study, different feature sets are used in conjunction with (k-nearest neighbors) k-NN and artificial neural network (ANN) classifiers to address the classification problem of respiratory sound signals. A comparison is made between the performances of k-NN and ANN classifiers with different feature sets derived from respiratory sound data acquired from one microphone placed on the posterior chest area. Each subject is represented by a single respiration cycle divided into sixty segments from which three different feature sets consisting of 6th order AR model coefficients, wavelet coefficients and crackle parameters in addition to AR model coefficients are extracted. Classification experiments are carried out on inspiration and expiration phases separately. The two class recognition problem between healthy and pathological subjects is addressed.