NLP-Based Feedback for Prostate Cancer

Overseen By Antwon Chaplin

Age: 18+

Sex: Male

Trial Phase: Academic

Sponsor: Cedars-Sinai Medical Center

Disqualifiers: Under 18, Dementia, Non-English, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 2

30 Participants Needed

Trial Summary

What is the purpose of this trial?

In this pilot study, the investigators will show feasibility of the NLP-based feedback system in 20 consultations of men with newly diagnosed prostate cancer. The investigators will recruit from the practices of up to 10 physicians who typically see these patients. The investigators will report the top five sentences from each consultation across key content areas (cancer prognosis, life expectancy, erectile dysfunction, urinary incontinence, and irritative urinary symptoms) to both patients and physicians within 2 weeks of the consultation.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the treatment NLP-based Feedback for prostate cancer?

The research suggests that natural language processing (NLP) can accurately assess important patient-centered outcomes like urinary incontinence and bowel dysfunction after prostate cancer treatment, which may help improve patient care by providing better feedback on treatment effects. ^{1 2 3 4 5}

Is NLP-based feedback generally safe for humans?

There is no specific safety data available for NLP-based feedback in humans, but NLP is used in healthcare to help identify medication-related issues and improve safety by analyzing large amounts of data. ^{6 7 8 9 10}

How is NLP-based Feedback treatment different from other prostate cancer treatments?

NLP-based Feedback treatment is unique because it uses natural language processing (NLP) to analyze clinical notes and assess patient-centered outcomes like urinary incontinence and bowel dysfunction after prostate cancer treatment. This approach focuses on improving the understanding of patient experiences and outcomes, which is different from traditional treatments that primarily target the cancer itself. ^{1 11 12 13 14}

Research Team

Timothy Daskivich

Principal Investigator

Cedars-Sinai Medical Center

Eligibility Criteria

This trial is for men who are having their first treatment talk for localized prostate cancer or those on active surveillance with worsening cancer considering local therapy. Participants must be patients at Cedars-Sinai and able to read and write in English.

Inclusion Criteria

Ability to read and write in English.

I am a man seeking initial treatment for prostate cancer that has not spread.

My prostate cancer has worsened while on active surveillance and I am considering treatment.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment Consultation

Audio recording and transcribing treatment counseling discussions for 20 men with newly diagnosed clinically localized prostate cancers

Up to 1 year

1 visit (in-person)

NLP Feedback and Assessment

NLP-based feedback provided to patients and physicians, followed by assessment of decisional conflict and risk perception

2 weeks

1 visit (virtual)

Follow-up

Participants are monitored for changes in decisional conflict and risk perception after receiving NLP-based feedback

4 weeks

Treatment Details

Interventions

• NLP-based Feedback

Trial Overview The study tests an NLP-based feedback system during consultations with men newly diagnosed with prostate cancer. It aims to improve communication by reporting key consultation points back to patients and doctors within two weeks.

Participant Groups

Group I: NLP Intervention Experimental Arm Experimental Treatment 1 Intervention

20 men with newly diagnosed clinically localized prostate cancers and utilize NLP to extract key content using the top five sentences by NLP probability for key content areas will be generated and will be provided to patients and providers within 2 weeks after each case.

Who Is Running the Clinical Trial?

Cedars-Sinai Medical Center

Lead Sponsor

Trials

523

Recruited

165,000+

National Cancer Institute (NCI)

Collaborator

Trials

14,080

Recruited

41,180,000+

Findings from Research

A new natural language processing (NLP) pipeline effectively identifies patient-centered outcomes (PCOs) like urinary incontinence and bowel dysfunction in clinical notes, achieving an impressive average F1 score of 0.86.

This machine learning approach outperformed existing rule-based models, demonstrating that it can accurately classify clinical notes without the need for manual review, making it a more efficient tool for assessing patient outcomes after prostate cancer treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment. Banerjee, I., Li, K., Seneviratne, M., et al.[2020]

Natural language processing (NLP) can significantly enhance cancer research by extracting valuable clinical data from unstructured text in electronic medical records, which can help in personalizing treatment options.

Oncologists can actively participate in developing NLP systems, which not only aids in cancer case identification and outcomes measurement but also promotes evidence-based research in oncology, potentially leading to improved cancer care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Natural Language Processing in Oncology: A Review. Yim, WW., Yetisgen, M., Harris, WP., et al.[2021]

Artificial neural networks and clinical prediction tools can effectively forecast various cancer-related outcomes, such as disease stage and survival rates, providing valuable information for physicians and patients.

There is a need for more models that also consider quality of life outcomes, and combining these predictions into a comprehensive table could help patients make better-informed treatment choices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prediction tools in surgical oncology. Isariyawongse, BK., Kattan, MW.[2019]

Natural language processing (NLP) and machine learning (ML) techniques are effectively used to analyze unstructured patient-reported outcome (PRO) data from electronic health records (EHRs), with 79 studies reviewed highlighting their potential in clinical care.

The majority of studies focused on extracting PROs from clinical narratives and using them for purposes like disease progression prediction and phenotyping, indicating a growing trend towards integrating advanced data analysis methods in healthcare.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Sim, JA., Huang, X., Horan, MR., et al.[2023]

Nomograms are effective tools that can help patients with prostate cancer and their doctors make informed treatment decisions by predicting individual outcomes based on multiple variables.

Currently, nomograms can predict progression-free survival after various treatments for localized prostate cancer, but there is a need for more models that also consider survival and quality of life.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Predicting clinical end points: treatment nomograms in prostate cancer. Di Blasio, CJ., Rhee, AC., Cho, D., et al.[2022]

A new standardized Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) subset was developed specifically for monitoring adverse events in prostate cancer patients, based on input from 30 patients and 16 healthcare providers.

The final subset includes 12 key adverse events such as erectile dysfunction, urinary issues, and emotional distress, with 90% consensus among participants, highlighting the importance of capturing patient experiences for better care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Selecting a PRO-CTCAE-based subset for patient-reported symptom monitoring in prostate cancer patients: a modified Delphi procedure. Feldman, E., Pos, FJ., Smeenk, RJ., et al.[2023]

In a study of 322,887 cancer patients undergoing treatment, 19% experienced oncology-specific adverse events, with higher rates in those with metastatic cancers (41% for colorectal and 50% for lung cancer).

The most common triggers for these adverse events included abnormal serum bicarbonate levels and blood transfusions, highlighting the need for targeted monitoring in cancer care to improve patient safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Developing a cancer-specific trigger tool to identify treatment-related adverse events using administrative data. Weingart, SN., Nelson, J., Koethe, B., et al.[2021]

The MOSES method, which incorporates the timing and severity of adverse events, was found to be more accurate than the traditional CTCAE system in predicting the impact of symptoms on quality of life in cervical cancer patients undergoing radiotherapy, with a study involving 300 patients.

In comparing two radiotherapy techniques, 3DCRT showed a higher cumulative MOSES score than IMRT, indicating potentially greater late toxicity, despite no significant difference in overall gastrointestinal toxicity grades between the two methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Months and Severity Score (MOSES) in a Phase III trial (PARCER): A new comprehensive method for reporting adverse events in oncology clinical trials. Ranjan, N., Chopra, S., Mangaj, A., et al.[2022]

Natural language processing (NLP) can significantly enhance medication safety by automating the identification of adverse events from various data sources, such as electronic health records and social media, which can lead to quicker detection and response to potential risks.

Despite its potential, the effectiveness of NLP in monitoring adverse events is currently limited by challenges in data sharing and interoperability between healthcare organizations, which hinders comprehensive surveillance across populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges. Wong, A., Plasek, JM., Montecalvo, SP., et al.[2019]

A retrospective study involving 400 cancer patients identified 790 clinical triggers in medical records, leading to the detection of 304 unique adverse events (AEs), highlighting the tool's potential to improve patient safety in oncology.

The original screening tool had a positive predictive value (PPV) of 0.40 for total AEs and 0.15 for preventable AEs, with modifications increasing the PPV to 0.48 for total AEs, suggesting that refining this tool could enhance its effectiveness in real-time AE detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Performance of a Trigger Tool for Identifying Adverse Events in Oncology. Lipitz-Snyderman, A., Classen, D., Pfister, D., et al.[2021]

A natural language processing (NLP) algorithm was developed and validated to identify metastatic prostate cancer (mPCa) patients from clinical notes, successfully identifying 76,082 out of 1,144,610 diagnosed veterans, demonstrating high specificity (0.979) and sensitivity (0.919).

The use of NLP significantly enhances the identification of mPCa patients compared to traditional methods, providing a reliable tool to improve cancer registry data and potentially facilitate access to innovative treatments for mPCa.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Ascertainment of Veterans With Metastatic Prostate Cancer in Electronic Health Records: Demonstrating the Case for Natural Language Processing. Alba, PR., Gao, A., Lee, KM., et al.[2021]

A deep learning model using BERT achieved a tumor response category (TRC) classification F1 score of 0.70, outperforming conventional algorithms and radiology technologist students, indicating its potential for effective analysis of free-text oncology reports.

While the BERT model performed comparably to medical students, it was still less effective than experienced radiologists, highlighting the challenges posed by lexical complexity and semantic ambiguities in oncology reports.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports. Fink, MA., Kades, K., Bischoff, A., et al.[2022]

Natural language processing (NLP) can effectively convert unstructured clinical information from patient reports into structured data, making it accessible for automated applications that can enhance patient care and reduce costs.

Recent advancements in NLP systems have shown promising results in clinical settings, with some systems already deployed, indicating a significant step forward in utilizing electronic clinical information for better healthcare outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Natural language processing and its future in medicine. Friedman, C., Hripcsak, G.[2019]

ChatGPT was able to answer all 59 queries related to prostate cancer, but its accuracy and quality were suboptimal, with an average F1 score of 0.426 and a General Quality Score of 3.62 out of 5, indicating room for improvement.

Given the limitations in accuracy and potential for misunderstandings, caution is advised when using ChatGPT as a source of patient information on prostate cancer, highlighting the need for further research on AI-generated content.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Can ChatGPT, an Artificial Intelligence Language Model, Provide Accurate and High-quality Patient Information on Prostate Cancer? Coskun, B., Ocakoglu, G., Yetemen, M., et al.[2023]

References

1. United States pubmed.ncbi.nlm.nih.gov

Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment. [2020]

2. United States pubmed.ncbi.nlm.nih.gov

Natural Language Processing in Oncology: A Review. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

Prediction tools in surgical oncology. [2019]

4. Netherlands pubmed.ncbi.nlm.nih.gov

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. [2023]

5. United States pubmed.ncbi.nlm.nih.gov

Predicting clinical end points: treatment nomograms in prostate cancer. [2022]

6. England pubmed.ncbi.nlm.nih.gov

Selecting a PRO-CTCAE-based subset for patient-reported symptom monitoring in prostate cancer patients: a modified Delphi procedure. [2023]

7. United States pubmed.ncbi.nlm.nih.gov

Developing a cancer-specific trigger tool to identify treatment-related adverse events using administrative data. [2021]

8. England pubmed.ncbi.nlm.nih.gov

Months and Severity Score (MOSES) in a Phase III trial (PARCER): A new comprehensive method for reporting adverse events in oncology clinical trials. [2022]

9. United States pubmed.ncbi.nlm.nih.gov

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges. [2019]

10. United States pubmed.ncbi.nlm.nih.gov

Performance of a Trigger Tool for Identifying Adverse Events in Oncology. [2021]

11. United States pubmed.ncbi.nlm.nih.gov

Ascertainment of Veterans With Metastatic Prostate Cancer in Electronic Health Records: Demonstrating the Case for Natural Language Processing. [2021]

12. United States pubmed.ncbi.nlm.nih.gov

Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports. [2022]

13. United States pubmed.ncbi.nlm.nih.gov

Natural language processing and its future in medicine. [2019]

14. United States pubmed.ncbi.nlm.nih.gov

Can ChatGPT, an Artificial Intelligence Language Model, Provide Accurate and High-quality Patient Information on Prostate Cancer? [2023]