Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a diagnostic RING trial.

AIMS: Mutation detection accuracy has been described extensively; however, it is surprising that pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples has not been systematically assessed in clinical context. We designed a RING trial to (i) investigate pre-PCR variability, (ii) correlate pre-PCR variation with EGFR/BRAF mutation testing accuracy and (iii) investigate causes for observed variation. METHODS: 13 molecular pathology laboratories were recruited. 104 blinded FFPE curls including engineered FFPE curls, cell-negative FFPE curls and control FFPE tissue samples were distributed to participants for pre-PCR processing and mutation detection. Follow-up analysis was performed to assess sample purity, DNA integrity and DNA quantitation. RESULTS: Rate of mutation detection failure was 11.9%. Of these failures, 80% were attributed to pre-PCR error. Significant differences in DNA yields across all samples were seen using analysis of variance (p

Multipair massive MIMO full-duplex relaying with MRC/MRT processing

We consider a multipair relay channel, where multiple sources communicate with multiple destinations with the help of a full-duplex (FD) relay station (RS). All sources and destinations have a single antenna, while the RS is equipped with massive arrays. We assume that the RS estimates the channels by using training sequences transmitted from sources and destinations. Then, it uses maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To significantly reduce the loop interference (LI) effect, we propose two massive MIMO processing techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the RS. We derive an exact achievable rate in closed-form and evaluate the system spectral efficiency. We show that, by doubling the number of antennas at the RS, the transmit power of each source and of the RS can be reduced by 1.5 dB if the pilot power is equal to the signal power and by 3 dB if the pilot power is kept fixed, while maintaining a given quality-of-service. Furthermore, we compare FD and half-duplex (HD) modes and show that FD improves significantly the performance when the LI level is low.

Patient-reported quality-of-life analysis of radium-223 dichloride from the phase III ALSYMPCA study

BACKGROUND: Radium-223 dichloride (radium-223), a first-in-class α-emitting radiopharmaceutical, is recommended in both pre- and post-docetaxel settings in patients with castration-resistant prostate cancer (CRPC) and symptomatic bone metastases based on overall survival benefit demonstrated in the phase III ALSYMPCA study. ALSYMPCA included prospective measurements of health-related quality of life (QOL) using two validated instruments: the general EuroQoL 5D (EQ-5D) and the disease-specific Functional Assessment of Cancer Therapy-Prostate (FACT-P).

PATIENTS AND METHODS: Analyses were conducted to determine treatment effects of radium-223 plus standard of care (SOC) versus placebo plus SOC on QOL using FACT-P and EQ-5D. Outcomes assessed were percentage of patients experiencing improvement, percentage of patients experiencing worsening, and mean QOL scores during the study.

RESULTS: Analyses were carried out on the intent-to-treat population of patients randomized to receive radium-223 (n = 614) or placebo (n = 307). The mean baseline EQ-5D utility and FACT-P total scores were similar between treatment groups. A significantly higher percentage of patients receiving radium-223 experienced meaningful improvement in EQ-5D utility score on treatment versus placebo {29.2% versus 18.5%, respectively; P = 0.004; odds ratio (OR) = 1.82 [95% confidence interval (CI) 1.21-2.74]}. Findings were similar for FACT-P total score [24.6% versus 16.1%, respectively; P = 0.020; OR = 1.70 (95% CI 1.08-2.65)]. A lower percentage of patients receiving radium-223 experienced meaningful worsening versus placebo measured by EQ-5D utility score and FACT-P total score. Prior docetaxel use and current bisphosphonate use did not affect these findings. Treatment was a significant predictor of EQ-5D utility score, with radium-223 associated with higher scores versus placebo (0.56 versus 0.50, respectively; P = 0.002). Findings were similar for FACT-P total score (99.08 versus 95.22, respectively; P = 0.004).

CONCLUSIONS: QOL data from ALSYMPCA demonstrated that improved survival with radium-223 is accompanied by significant QOL benefits, including a higher percentage of patients with meaningful QOL improvement and a slower decline in QOL over time in patients with CRPC.

FPGA-based soft-core processors for image processing applications

With security and surveillance, there is an increasing need to process image data efficiently and effectively either at source or in a large data network. Whilst a Field-Programmable Gate Array (FPGA) has been seen as a key technology for enabling this, the design process has been viewed as problematic in terms of the time and effort needed for implementation and verification. The work here proposes a different approach of using optimized FPGA-based soft-core processors which allows the user to exploit the task and data level parallelism to achieve the quality of dedicated FPGA implementations whilst reducing design time. The paper also reports some preliminary
progress on the design flow to program the structure. An implementation for a Histogram of Gradients algorithm is also reported which shows that a performance of 328 fps can be achieved with this design approach, whilst avoiding the long design time, verification and debugging steps associated with conventional FPGA implementations.