Gupta S

Background: Colorectal cancer (CRC) surveillance guidelines suggest that timing of a 3rd colonoscopy should be based on results of two prior exams. However, data are limited on whether baseline screening colonoscopy can inform the risk of advanced neoplasia (AN) at 3rd exam.

Methods: This study describes the risk of AN at 3rd colonoscopy stratified by findings on two previous exams in a prospective screening cohort and compares this risk over time from a negative 2nd exam between those with differing 1st exam findings.

The CSP #380 cohort included 3,121 Veterans aged 50-75 years who underwent screening colonoscopy from 1994-1997 and were followed for at least 10 years. Exclusion criteria included not having three colonoscopies more than one year apart, or having CRC at 1st or 2nd exam. The primary outcome was the proportion of AN at 3rd exam. Findings at 1st and 2nd exam were classified as high-risk adenoma (HRA), low-risk adenoma (LRA), or no adenoma. Chi-square tests compared proportions of AN on the 3rd exam between those with different baseline screening results but similar 2nd exam findings.

Results: This analysis included 891 participants: 58 (6.5%) had AN at 3rd exam. The proportion of AN at 3rd exam ranged from 3.2% to 21.4% when stratified by results of two previous exams. In participants with HRA or LRA on the 2nd exam, baseline screening colonoscopy was not associated with risk of AN at 3rd exam. However, for participants with no adenomas on the 2nd exam, baseline screening colonoscopy was associated with risk of AN at 3rd exam (P =.04). Furthermore, all AN was identified within about 5 years of the negative 2nd exam in those with HRA on the 1st exam.

Conclusions: Results of the 1st exam remain a risk factor for AN at 3rd exam in those with no adenomas at 2nd exam. This supports current guidelines which recommend a shortened surveillance interval in those with no adenomas at 2nd exam but HRA at 1st. Future work will combine CRC risk factors with genomic risk and colonoscopy outcomes over time to better identify individuals who might benefit from continued surveillance and to help inform appropriate surveillance intervals.

Background: Colorectal cancer (CRC) surveillance guidelines suggest that timing of a 3rd colonoscopy should be based on results of two prior exams. However, data are limited on whether baseline screening colonoscopy can inform the risk of advanced neoplasia (AN) at 3rd exam.

Methods: This study describes the risk of AN at 3rd colonoscopy stratified by findings on two previous exams in a prospective screening cohort and compares this risk over time from a negative 2nd exam between those with differing 1st exam findings.

The CSP #380 cohort included 3,121 Veterans aged 50-75 years who underwent screening colonoscopy from 1994-1997 and were followed for at least 10 years. Exclusion criteria included not having three colonoscopies more than one year apart, or having CRC at 1st or 2nd exam. The primary outcome was the proportion of AN at 3rd exam. Findings at 1st and 2nd exam were classified as high-risk adenoma (HRA), low-risk adenoma (LRA), or no adenoma. Chi-square tests compared proportions of AN on the 3rd exam between those with different baseline screening results but similar 2nd exam findings.

Results: This analysis included 891 participants: 58 (6.5%) had AN at 3rd exam. The proportion of AN at 3rd exam ranged from 3.2% to 21.4% when stratified by results of two previous exams. In participants with HRA or LRA on the 2nd exam, baseline screening colonoscopy was not associated with risk of AN at 3rd exam. However, for participants with no adenomas on the 2nd exam, baseline screening colonoscopy was associated with risk of AN at 3rd exam (P =.04). Furthermore, all AN was identified within about 5 years of the negative 2nd exam in those with HRA on the 1st exam.

Conclusions: Results of the 1st exam remain a risk factor for AN at 3rd exam in those with no adenomas at 2nd exam. This supports current guidelines which recommend a shortened surveillance interval in those with no adenomas at 2nd exam but HRA at 1st. Future work will combine CRC risk factors with genomic risk and colonoscopy outcomes over time to better identify individuals who might benefit from continued surveillance and to help inform appropriate surveillance intervals.

Background: Colorectal cancer (CRC) surveillance guidelines suggest that timing of a 3rd colonoscopy should be based on results of two prior exams. However, data are limited on whether baseline screening colonoscopy can inform the risk of advanced neoplasia (AN) at 3rd exam.

Methods: This study describes the risk of AN at 3rd colonoscopy stratified by findings on two previous exams in a prospective screening cohort and compares this risk over time from a negative 2nd exam between those with differing 1st exam findings.

The CSP #380 cohort included 3,121 Veterans aged 50-75 years who underwent screening colonoscopy from 1994-1997 and were followed for at least 10 years. Exclusion criteria included not having three colonoscopies more than one year apart, or having CRC at 1st or 2nd exam. The primary outcome was the proportion of AN at 3rd exam. Findings at 1st and 2nd exam were classified as high-risk adenoma (HRA), low-risk adenoma (LRA), or no adenoma. Chi-square tests compared proportions of AN on the 3rd exam between those with different baseline screening results but similar 2nd exam findings.

Results: This analysis included 891 participants: 58 (6.5%) had AN at 3rd exam. The proportion of AN at 3rd exam ranged from 3.2% to 21.4% when stratified by results of two previous exams. In participants with HRA or LRA on the 2nd exam, baseline screening colonoscopy was not associated with risk of AN at 3rd exam. However, for participants with no adenomas on the 2nd exam, baseline screening colonoscopy was associated with risk of AN at 3rd exam (P =.04). Furthermore, all AN was identified within about 5 years of the negative 2nd exam in those with HRA on the 1st exam.

Conclusions: Results of the 1st exam remain a risk factor for AN at 3rd exam in those with no adenomas at 2nd exam. This supports current guidelines which recommend a shortened surveillance interval in those with no adenomas at 2nd exam but HRA at 1st. Future work will combine CRC risk factors with genomic risk and colonoscopy outcomes over time to better identify individuals who might benefit from continued surveillance and to help inform appropriate surveillance intervals.

Background: Cancer care in academically affiliated settings such as teaching hospitals has been associated with improved clinical outcomes. Historically, Veterans Affairs (VA) medical centers are partnered with academic affiliates; however, there have been few studies examining how this partnership affects clinical care in the Veterans Health Administration (VHA). We therefore examined the variation of first line therapy (1L) in patients with metastatic castrate resistant prostate cancer (mCRPC) in the VHA by degree of academic affiliation.

Methods: Information from the VA Central Cancer Registry was linked to clinical data from the VA Corporate Data Warehouse to identify incident cases of mCRPC, defined as first incidence of radiologic evidence of metastasis and castrate resistance in patients with prostate cancer. Patient demographics, disease characteristics and treatment practices were extracted. The degree of academic affiliation of the treating facility was calculated using the Herfindahl-Hirschman Index (HHI), which reflects how dispersed medical residents are among different specialties and how many specialties are available within a given VA facility.

Results: From 2006 to 2015, 3,637 patients received an mCRPC diagnosis and were treated in 123 VA facilities. Median HHI for treating facilities was 0.374. Of these patients, 1,723 (47%) were treated in a facility with higher academic affiliation (HAA; HHI ≥ 0.374) and 1,914 (53%) were treated in a facility with lower academic affiliation (LAA; HHI ≤ 0.373). There was no difference in patient or disease characteristics by academic affiliation; patients with HAA and LAA had comparable Gleason scores, stage of disease at diagnosis, primary local therapy, age and median PSA levels at time of diagnosis. Patients with mCRPC at HAA facilities were more likely to receive 1L (59% vs 55%, P = .015). Regimens frequently used for 1L were comparable: HAA, docetaxel (29%), abiraterone (22%), and enzalutamide (6%); LAA: docetaxel (25%), abiraterone (21%), and enzalutamide (7%).

Conclusions: Patients with mCRPC had a small but significant increase in likelihood of receiving 1L if treated in HAA vs LAA facilities. Further study will focus on identifying patient, prescriber and facility factors that are associated with the likelihood of initiating 1L and the choice of 1L regimen.

Background: Cancer care in academically affiliated settings such as teaching hospitals has been associated with improved clinical outcomes. Historically, Veterans Affairs (VA) medical centers are partnered with academic affiliates; however, there have been few studies examining how this partnership affects clinical care in the Veterans Health Administration (VHA). We therefore examined the variation of first line therapy (1L) in patients with metastatic castrate resistant prostate cancer (mCRPC) in the VHA by degree of academic affiliation.

Methods: Information from the VA Central Cancer Registry was linked to clinical data from the VA Corporate Data Warehouse to identify incident cases of mCRPC, defined as first incidence of radiologic evidence of metastasis and castrate resistance in patients with prostate cancer. Patient demographics, disease characteristics and treatment practices were extracted. The degree of academic affiliation of the treating facility was calculated using the Herfindahl-Hirschman Index (HHI), which reflects how dispersed medical residents are among different specialties and how many specialties are available within a given VA facility.

Results: From 2006 to 2015, 3,637 patients received an mCRPC diagnosis and were treated in 123 VA facilities. Median HHI for treating facilities was 0.374. Of these patients, 1,723 (47%) were treated in a facility with higher academic affiliation (HAA; HHI ≥ 0.374) and 1,914 (53%) were treated in a facility with lower academic affiliation (LAA; HHI ≤ 0.373). There was no difference in patient or disease characteristics by academic affiliation; patients with HAA and LAA had comparable Gleason scores, stage of disease at diagnosis, primary local therapy, age and median PSA levels at time of diagnosis. Patients with mCRPC at HAA facilities were more likely to receive 1L (59% vs 55%, P = .015). Regimens frequently used for 1L were comparable: HAA, docetaxel (29%), abiraterone (22%), and enzalutamide (6%); LAA: docetaxel (25%), abiraterone (21%), and enzalutamide (7%).

Conclusions: Patients with mCRPC had a small but significant increase in likelihood of receiving 1L if treated in HAA vs LAA facilities. Further study will focus on identifying patient, prescriber and facility factors that are associated with the likelihood of initiating 1L and the choice of 1L regimen.

Background: Cancer care in academically affiliated settings such as teaching hospitals has been associated with improved clinical outcomes. Historically, Veterans Affairs (VA) medical centers are partnered with academic affiliates; however, there have been few studies examining how this partnership affects clinical care in the Veterans Health Administration (VHA). We therefore examined the variation of first line therapy (1L) in patients with metastatic castrate resistant prostate cancer (mCRPC) in the VHA by degree of academic affiliation.

Methods: Information from the VA Central Cancer Registry was linked to clinical data from the VA Corporate Data Warehouse to identify incident cases of mCRPC, defined as first incidence of radiologic evidence of metastasis and castrate resistance in patients with prostate cancer. Patient demographics, disease characteristics and treatment practices were extracted. The degree of academic affiliation of the treating facility was calculated using the Herfindahl-Hirschman Index (HHI), which reflects how dispersed medical residents are among different specialties and how many specialties are available within a given VA facility.

Results: From 2006 to 2015, 3,637 patients received an mCRPC diagnosis and were treated in 123 VA facilities. Median HHI for treating facilities was 0.374. Of these patients, 1,723 (47%) were treated in a facility with higher academic affiliation (HAA; HHI ≥ 0.374) and 1,914 (53%) were treated in a facility with lower academic affiliation (LAA; HHI ≤ 0.373). There was no difference in patient or disease characteristics by academic affiliation; patients with HAA and LAA had comparable Gleason scores, stage of disease at diagnosis, primary local therapy, age and median PSA levels at time of diagnosis. Patients with mCRPC at HAA facilities were more likely to receive 1L (59% vs 55%, P = .015). Regimens frequently used for 1L were comparable: HAA, docetaxel (29%), abiraterone (22%), and enzalutamide (6%); LAA: docetaxel (25%), abiraterone (21%), and enzalutamide (7%).

Conclusions: Patients with mCRPC had a small but significant increase in likelihood of receiving 1L if treated in HAA vs LAA facilities. Further study will focus on identifying patient, prescriber and facility factors that are associated with the likelihood of initiating 1L and the choice of 1L regimen.

Background: Radiographic imaging is important for the diagnosis and management of cancer. Radiology reports contain a wealth of information, but are typically formatted as unstructured text, making large scale information extraction challenging. We validated a natural language processing (NLP) algorithm to identify the presence of metastatic disease in radiographic imaging reports.

Methods: Using VA Clinical Cancer Registry and Corporate Data Warehouse, we identified approximately 3 million radiology reports for 120,374 patients receiving care for prostate cancer in the VA from 2006-2015. We focused on the impression section of CT, PET/CT, X-ray, bone scan, and MRI reports. We expanded on Chapman et al. “ConText” algorithm to identify the presence of metastatic disease: (1) Using UMLS, we identified terms compatible with “metastasis”; (2) Report impressions were preprocessed and tokenized at the sentence level and as part of the sentence; (3) Positive and negative trigger phrases were implemented as a series of regular expressions, which were refined over a number of iterations using training data from 2 batches of 600 reports, allowing us to extend trigger identification to a larger set of phrases. The final algorithm was validated using an independent sample of 2,000 reports annotated by a domain expert.

Results: The first training set of 600 of radiology reports achieved an accuracy of: 94% for reports with no mention of metastasis, 85% for negated mention of metastasis, and 74% mentions of metastasis without negation. Errors were reviewed resulting in vocabulary expansion and improved implementation of regular expressions to capture the expanded trigger phrases. Performance of the modified algorithm was tested on a new set of 600 reports and resulted in an increased accuracy of 96% for no mention of metastasis, 90% for negated mention of metastasis, and 89% mentions of metastasis without negation. After additional modifications were made, the revised algorithm was validated using an independent sample of 2,000 reports. The accuracy was 96% (Cohen’s kappa ~1), with precision of 98%, and a sensitivity of 98%.

Conclusions: Detecting presence of metastatic disease from radiographic notes is feasible with NLP.

References: (1) Sarkar S, Das S. A review of imaging methods for prostate cancer detection. Biomed Eng Comput Biol. 2016;7(Suppl 1):1-15. (2) Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001;34(5):301- 310. (3) Harkema H, Dowling JN, Thornblade T. Con-Text: An algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform. 2009;42(5):839-851.

Background: Radiographic imaging is important for the diagnosis and management of cancer. Radiology reports contain a wealth of information, but are typically formatted as unstructured text, making large scale information extraction challenging. We validated a natural language processing (NLP) algorithm to identify the presence of metastatic disease in radiographic imaging reports.

Methods: Using VA Clinical Cancer Registry and Corporate Data Warehouse, we identified approximately 3 million radiology reports for 120,374 patients receiving care for prostate cancer in the VA from 2006-2015. We focused on the impression section of CT, PET/CT, X-ray, bone scan, and MRI reports. We expanded on Chapman et al. “ConText” algorithm to identify the presence of metastatic disease: (1) Using UMLS, we identified terms compatible with “metastasis”; (2) Report impressions were preprocessed and tokenized at the sentence level and as part of the sentence; (3) Positive and negative trigger phrases were implemented as a series of regular expressions, which were refined over a number of iterations using training data from 2 batches of 600 reports, allowing us to extend trigger identification to a larger set of phrases. The final algorithm was validated using an independent sample of 2,000 reports annotated by a domain expert.

Results: The first training set of 600 of radiology reports achieved an accuracy of: 94% for reports with no mention of metastasis, 85% for negated mention of metastasis, and 74% mentions of metastasis without negation. Errors were reviewed resulting in vocabulary expansion and improved implementation of regular expressions to capture the expanded trigger phrases. Performance of the modified algorithm was tested on a new set of 600 reports and resulted in an increased accuracy of 96% for no mention of metastasis, 90% for negated mention of metastasis, and 89% mentions of metastasis without negation. After additional modifications were made, the revised algorithm was validated using an independent sample of 2,000 reports. The accuracy was 96% (Cohen’s kappa ~1), with precision of 98%, and a sensitivity of 98%.

Conclusions: Detecting presence of metastatic disease from radiographic notes is feasible with NLP.

References: (1) Sarkar S, Das S. A review of imaging methods for prostate cancer detection. Biomed Eng Comput Biol. 2016;7(Suppl 1):1-15. (2) Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001;34(5):301- 310. (3) Harkema H, Dowling JN, Thornblade T. Con-Text: An algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform. 2009;42(5):839-851.

Background: Radiographic imaging is important for the diagnosis and management of cancer. Radiology reports contain a wealth of information, but are typically formatted as unstructured text, making large scale information extraction challenging. We validated a natural language processing (NLP) algorithm to identify the presence of metastatic disease in radiographic imaging reports.

Methods: Using VA Clinical Cancer Registry and Corporate Data Warehouse, we identified approximately 3 million radiology reports for 120,374 patients receiving care for prostate cancer in the VA from 2006-2015. We focused on the impression section of CT, PET/CT, X-ray, bone scan, and MRI reports. We expanded on Chapman et al. “ConText” algorithm to identify the presence of metastatic disease: (1) Using UMLS, we identified terms compatible with “metastasis”; (2) Report impressions were preprocessed and tokenized at the sentence level and as part of the sentence; (3) Positive and negative trigger phrases were implemented as a series of regular expressions, which were refined over a number of iterations using training data from 2 batches of 600 reports, allowing us to extend trigger identification to a larger set of phrases. The final algorithm was validated using an independent sample of 2,000 reports annotated by a domain expert.

Results: The first training set of 600 of radiology reports achieved an accuracy of: 94% for reports with no mention of metastasis, 85% for negated mention of metastasis, and 74% mentions of metastasis without negation. Errors were reviewed resulting in vocabulary expansion and improved implementation of regular expressions to capture the expanded trigger phrases. Performance of the modified algorithm was tested on a new set of 600 reports and resulted in an increased accuracy of 96% for no mention of metastasis, 90% for negated mention of metastasis, and 89% mentions of metastasis without negation. After additional modifications were made, the revised algorithm was validated using an independent sample of 2,000 reports. The accuracy was 96% (Cohen’s kappa ~1), with precision of 98%, and a sensitivity of 98%.

Conclusions: Detecting presence of metastatic disease from radiographic notes is feasible with NLP.

References: (1) Sarkar S, Das S. A review of imaging methods for prostate cancer detection. Biomed Eng Comput Biol. 2016;7(Suppl 1):1-15. (2) Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001;34(5):301- 310. (3) Harkema H, Dowling JN, Thornblade T. Con-Text: An algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform. 2009;42(5):839-851.