The number of negative pelvic lymph nodes removed does not affect the risk of biochemical failure after radical prostatectomy

Study Type – Therapy (case series)
Level of Evidence 4

OBJECTIVES

To assess patients who had radical prostatectomy (RP) and pelvic lymph node dissection (PLND) for pT2–4 N0M0 prostate cancer, to determine if LN yield affects the risk of biochemical failure (BCF), as the extent of PLND at the time of RP has become increasingly uncertain with the decreasing trend in tumour stage.

PATIENTS AND METHODS

We reviewed the Columbia University Urologic Oncology Database for patients with pT2–4 N0M0 prostate cancer treated with RP from 1990 to 2005. Exclusion criteria included <12 months of follow‐up, incomplete clinical and pathological data, and neoadjuvant androgen‐deprivation therapy (ADT) or immediate adjuvant ADT or external beam radiotherapy. Unadjusted and adjusted models were used to determine the ability of clinical and pathological variables to predict BCF.

RESULTS

The final dataset included 964 patients, with a mean age of 60.5 years and median preoperative prostate‐specific antigen (PSA) level of 6.2 ng/mL. The median (range) LN yield was 7 (1–42) and the median follow‐up 59 (12–190) months. In the unadjusted and adjusted models, preoperative PSA, pathological Gleason score, pathological stage, surgical margin status and year of surgery were significant predictors of BCF. The LN group was not a significant predictor of BCF in both the unadjusted and adjusted model (P = 0.759 and 0.408, respectively). When patients were stratified into high‐ and low‐risk groups, LN yield remained an insignificant predictor of BCF.

CONCLUSION

A higher LN yield at the time of RP does not increase the chance of cure for patients with pT2–4N0M0 prostate cancer. This lack of a survival advantage holds true for patients with high‐risk disease.     

Evaluating computer-aided detection algorithms

Computer-aided detection (CAD) has been attracting extensive research interest during the last two decades. It is recognized that the full potential of CAD can only be realized by improving the performance and robustness of CAD algorithms and this requires good evaluation methodology that would permit CAD designers to optimize their algorithms. Free-response receiver operating characteristic (FROC) curves are widely used to assess CAD performance, however, evaluation rarely proceeds beyond determination of lesion localization fraction (sensitivity) at an arbitrarily selected value of nonlesion localizations (false marks) per image. This work describes a FROC curve fitting procedure that uses a recent model of visual search that serves as a framework for the free-response task. A maximum likelihood procedure for estimating the parameters of the model from free-response data and fitting CAD generated FROC curves was implemented. Procedures were implemented to estimate two figures of merit and associated statistics such as 95% confidence intervals and goodness of fit. One of the figures of merit does not require the arbitrary specification of an operating point at which to evaluate CAD performance. For comparison a related method termed initial detection and candidate analysis was also implemented that is applicable when all suspicious regions are reported. The two methods were tested on seven mammography CAD data sets and both yielded good to excellent fits. The search model approach has the advantage that it can potentially be applied to radiologist generated free-response data where not all suspicious regions are reported, only the ones that are deemed sufficiently suspicious to warrant clinical follow-up. This work represents the first practical application of the search model to an important evaluation problem in diagnostic radiology. Software based on this work is expected to benefit CAD developers working in diverse areas of medical imaging.     

Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation

The authors have developed a semiautomatic system for segmentation of a diverse set of lesions in head and neck CT scans. The system takes as input an approximate bounding box, and uses a multistage level set to perform the final segmentation. A data set consisting of 69 lesions marked on 33 scans from 23 patients was used to evaluate the performance of the system. The contours from automatic segmentation were compared to both 2D and 3D gold standard contours manually drawn by three experienced radiologists. Three performance metric measures were used for the comparison. In addition, a radiologist provided quality ratings on a 1 to 10 scale for all of the automatic segmentations. For this pilot study, the authors observed that the differences between the automatic and gold standard contours were larger than the interobserver differences. However, the system performed comparably to the radiologists, achieving an average area intersection ratio of 85.4% compared to an average of 91.2% between two radiologists. The average absolute area error was 21.1% compared to 10.8%, and the average 2D distance was 1.38 mm compared to 0.84 mm between the radiologists. In addition, the quality rating data showed that, despite the very lax assumptions made on the lesion characteristics in designing the system, the automatic contours approximated many of the lesions very well.     

Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms

We have developed a computer-aided detection (CAD) system to detect clustered microcalcifications automatically on full-field digital mammograms (FFDMs) and a CAD system for screen-film mammograms (SFMs). The two systems used the same computer vision algorithms but their false positive (FP) classifiers were trained separately with sample images of each modality. In this study, we compared the performance of the CAD systems for detection of clustered microcalcifications on pairs of FFDM and SFM obtained from the same patient. For case-based performance evaluation, the FFDM CAD system achieved detection sensitivities of 70%, 80% and 90% at an average FP cluster rate of 0.07, 0.16 and 0.63 per image, compared with an average FP cluster rate of 0.15, 0.38 and 2.02 per image for the SFM CAD system. The difference was statistically significant with the alternative free-response receiver operating characteristic (AFROC) analysis. When evaluated on data sets negative for microcalcification clusters, the average FP cluster rates of the FFDM CAD system were 0.04, 0.11 and 0.33 per image at detection sensitivity level of 70%, 80% and 90% compared with an average FP cluster rate of 0.08, 0.14 and 0.50 per image for the SFM CAD system. When evaluated for malignant cases only, the difference of the performance of the two CAD systems was not statistically significant with AFROC analysis.     

Relapsing polychondritis: reversible airway obstruction is not always asthma

Relapsing polychondritis (RP) is a rare disease characterized by recurrent inflammation of cartilaginous and other proteoglycan-rich tissues. Respiratory tract involvement is a common cause of morbidity and mortality in RP. We describe a patient whose clinical features at onset of disease were typical of asthma. Later, the patient developed symptoms and signs characteristic of RP. Tracheobronchomalacia necessitated airway support by stenting. The possibility that airway obstruction in the initial stages of RP is due to airway inflammation and that early, aggressive immunosuppressive treatment of RP may delay or prevent irreversible cartilaginous destruction and airway collapse are discussed.