Lung CancerOCTAPUS-AINCT04721444
Optimising Cancer Therapy And Identifying Causes of Pneumonitis USing Artificial Intelligence (COVID-19)
An observational study of Lung Cancer and Covid19, run by Royal Marsden NHS Foundation Trust at 3 sites in United Kingdom.
Trial summary
Distinguishing changes on patients that have received thoracic radiotherapy and patients that are currently receiving or have recently received IO and presenting lung changes which will be identified using AI. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer, Covid19, Pneumonitis
- What is being tested?
- Machine Learning Classification of parenchymal lung change cause and Machine Learning Classification of recurrence and non-recurrence
- How many people
- 1,211 participants
- Where
- 3 sitesUnited Kingdom
- Study dates
- 27 Jan 2021to 1 Mar 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: Arms A and B: Adult patients with pneumonitis that have previously been treated with thoracic radiotherapy or immunotherapy Arm C: Adult patients that have received radical radiotherapy for NSCLC
Who may be able to join
- Cohort A1 (from Arm A) - Immunotherapy (IO) pneumonitis cases: patients currently on or having received ICI IO in the last 3 months of presentation with:
- New radiological lung changes on CT/CXR (confirmed on report) of a severity and distribution consistent with IO pneumonitis. These changes should be of severity and distribution that are not incompatible with viral or lower respiratory tract infection.
- AND Must not have had RT involving the thorax (unless this was breast/chest wall RT more than 5 years ago, which is permissible) AND
- Where there is documented clinical concern for infection, have undergone one or more laboratory investigations for viral or lower respiratory tract infection including, but not limited to Nasopharyngeal aspirate or swab for respiratory virus by PCR; Sputum sample or bronchial washings MCS with no organism(s) consistent with lower respiratory tract infection, cytology or beta-glucan/galactomannan for PCP or fungal infection; broncho-alveolar lavage for markers of infection such as MCS, PCR, fungal culture, beta-glucan/galactomannan for PCP or evidence of lower respiratory tract infection (including invasive fungal infection) by cytology, none of which were considered positive for infection by the clinical team.
- Where empirical antibiotics were prescribed, patients must either have had a negative BAL infection screen or may be included at the discretion of the local site PI and local radiologist with lung interest or two members of the trial management group, one of whom must be a radiologist with lung interest or respiratory physician or oncologist with suitable experience of thoracic CT imaging, after after review of the case-notes.
- Prophylactic co-trimoxazole prescribed in the context of high-dose steroid therapy is permitted.
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- Cohort A2 (from Arm B) - Radiotherapy (RT) pneumonits cases: Patients that have completed a course of RT involving the thorax (e.g. lung, breast, oesophageal RT) in the last 12 months prior to presentation, that have not received immunotherapy, with:.
- New radiological lung changes on CT/CXR (confirmed on report) of a severity and distribution consistent with radiation pneumonitis or early fibrosis (should not include established fibrosis). These changes should be of severity and distribution that are not incompatible with viral or lower respiratory tract infection.
- AND
- Where there is documented clinical concern for infection, have undergone one or more laboratory investigations for viral or lower respiratory tract infection including, but not limited to Nasopharyngeal aspirate or swab for respiratory virus by PCR; Sputum sample or bronchial washings MCS with no organism(s) consistent with lower respiratory tract infection, cytology or beta-glucan/galactomannan for PCP or fungal infection; broncho-alveolar lavage (BAL) for markers of infection such as MCS, PCR, fungal culture, beta-glucan/galactomannan for Pneumocystis Pneumonia (PCP) or evidence of lower respiratory tract infection (including invasive fungal infection) by cytology, none of which were considered positive for infection by the clinical team. Where empirical antibiotics were prescribed, patients must either have had a negative BAL infection screen or may be included at the discretion of the local site PI and local radiologist with lung interest or two members of the trial management group, one of whom must be a radiologist with lung interest or respiratory physician or oncologist with suitable experience of thoracic CT imaging, after review of the case-notes.
- Prophylactic co-trimoxazole prescribed in the context of high-dose steroid therapy is permitted.
- B1 (Utilised in Arms A \& B) Non-COVID-19 infective cases:
- New radiological lung changes on CT/CXR (confirmed on report) of a severity and distribution consistent with lower respiratory tract infection but compatible with the grade and nature of pneumonitis seen with IO or RT
- AND
- Laboratory findings that fulfil one or more of the following criteria of infection: Nasopharyngeal aspirate or swab positive for a respiratory virus by PCR; Sputum sample or bronchial washings positive MCS for an organism(s) consistent with lower respiratory tract infection, cytology or beta-glucan/galactomannan positive for PCP or fungal infection, positive urine legionella/pneumococcal antigen screen, positive serology for mycoplasma pneumonia; broncho-alveolar lavage for markers of infection (MCS, PCR, fungal culture, beta-glucan/galactomannan for PCP or other evidence of lower respiratory tract infection (including invasive fungal infection) by cytology. Where no such laboratory findings were positive but the patient improved with anti-microbial therapy, such cases may be included at the discretion of the local site PI and local radiologist with lung interest or two members of the trial management group two members of the trial management group, one of whom must be a radiologist with a lung interest or respiratory physician or oncologist with suitable experience of thoracic CT imaging, after review of the case-notes and imaging.
- Not previously treated with immunotherapy OR
- Must not have had RT involving the thorax (unless this was breast/chest wall RT more than 5 years ago, which is permissible)
- First assessed prior to 1st January 2020 (and therefore not attributable to COVID-19)
- B2 (Utilised in Arms A \& B) COVID-19 cases:
- Laboratory findings that fulfil one or more of the following criteria of COVID-19 infection: positive COVID-19 PCR test and/or antigen test or other suitable assay that indicates current infection or previous exposure (including serology tests) as determined by the trial management group (TMG).
- AND
- New radiological lung changes on CT/CXR (confirmed on report) of a severity and distribution consistent with COVID-19. These changes should be of severity and distribution that is not incompatible with the grade of pneumonitis seen with IO or RT
- Not previously treated with immunotherapy OR
- Must not have had RT involving the thorax (unless this was breast/chest wall RT more than 5 years ago, which is permissible)
- Assessed after 1st January 2020 (and therefore contemporaneous with COVID-19)
- Adult patients (aged 18 or over) treated with radical thoracic RT (conventional fractionated RT +/- chemotherapy or SBRT) for NSCLC
- RT planning scan imaging and labelled structure set data available from participating centre
- Minimum 2 years of post-RT follow-up data including clinical or histological confirmation in the case of recurrence and whether the patient is alive as available from primary care or hospital records.
- Patients with post-treatment surveillance CT imaging (minimum of first scan post-treatment and where available +/- further scans within 2 years post-RT, e.g. at 3/6/12 months post-treatment).
Who cannot join
- Patients with documented past medical history of congestive cardiac failure or other cause for interstitial lung disease
- Arm C:
- Any patient that does not have a primary lung mass e.g. Tx disease
- Any patient being treated for recurrence of a previously treated lung cancer
- Any patient that did not have radical RT e.g. patients that had high dose palliative RT
- Any patient that does not have imaging that meets technical requirements within the imaging processing and analysis manual
Trial locations and contacts
3 sites in 1 country.
United Kingdom3 sites
Guys and St. Thomas' NHS Foundation Trust
London
Imperial College Healthcare NHS Trust
London
Royal Marsden NHS Foundation Trust
London
About this study
Official title: Optimising Cancer Therapy And Identifying Causes of Pneumonitis USing Artificial Intelligence
Distinguishing changes on patients that have received thoracic radiotherapy and patients that are currently receiving or have recently received IO and presenting lung changes which will be identified using AI.
- Conditions
- Lung Cancer, Covid19, Pneumonitis
- Being tested
- Machine Learning Classification of parenchymal lung change cause, Machine Learning Classification of recurrence and non-recurrence
- Medical subject headings
- Lung Neoplasms, COVID-19, Pneumonia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine Learning Classification of parenchymal lung change causeDiagnostic test
Arms A \& B: Radiomics and deep-learning approaches will be used on patient's imaging to develop a feature vector that can distinguish parenchymal lung changes, e.g. infection from drug-toxicity.
Machine Learning Classification of recurrence and non-recurrenceDiagnostic test
Arm C: Radiomics and deep-learning approaches will be used on patient's imaging to develop a risk-signature for recurrence of malignancy following radical treatment
| Study group | Role | What they receive |
|---|---|---|
| Arm A - Cohort A1 | Training set: Pneumonitis in the context of IO therapy and negative for infectious pneumonia (including COVID-19) Receives: Diagnostic Test: Machine Learning Classification of parenchymal lung change cause | |
| Arms A and B - Cohort B1 | Training set B1: IO and RT naive and pneumonia (without COVID-19) Receives: Diagnostic Test: Machine Learning Classification of parenchymal lung change cause | |
| Arms A and B - Cohort B2 | Training set B2: IO and RT naive and confirmed COVID-19 positive with pneumonia Receives: Diagnostic Test: Machine Learning Classification of parenchymal lung change cause | |
| Arm B - Cohort A2 | Training set: Pneumonitis in the context of thoracic RT and negative for infectious pneumonia (including COVID-19) Receives: Diagnostic Test: Machine Learning Classification of parenchymal lung change cause | |
| Arm A - Test Cohort (Cohort C1) | Test set C1: Patients on IO and with possible toxicity versus COVID-19 or other infective pneumonitis Receives: Diagnostic Test: Machine Learning Classification of parenchymal lung change cause | |
| Arm B - Test Cohort (Cohort C2) | Test set C2: Patients with pneumonitis in context of thoracic RT with possible toxicity versus COVID-19 or other infective pneumonitis. Receives: Diagnostic Test: Machine Learning Classification of parenchymal lung change cause | |
| Arm C | Patients with radiotherapy planning CT scans and post-treatment surveillance CT scans at 3, 6 and 12-months post treatment Receives: Diagnostic Test: Machine Learning Classification of recurrence and non-recurrence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Development of a Machine Learning model to distinguish parenchymal lung changes
The development and validation of an ML/radiomic classifier to distinguish between Infective/COVID-19 pneumonia and cancer therapy induced lung changes
Measured: 3 years
Development of a Machine Learning model to predict recurrence risk after radical radiotherapy for non-small cell lung cancer
to develop a prognostic AI/radiomic signature for NSCLC recurrence after radical RT (Conventional fractionated RT +/- chemotherapy or stereotactic body RT (SBRT)) to stratify appropriate surveillance and onward care, thus minimising unnecessary hospital visits and resource use.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jan 2021
- Study start27 Jan 2021
- Primary completion1 Mar 2022
- Study completion1 Mar 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Royal Marsden NHS Foundation TrustThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,211 participants (actual)
Sponsor and investigators
- Lead sponsor
- Royal Marsden NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Richard Lee, Principal investigator, Royal Marsden NHS Foundation Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04721444
- Sponsor's protocol ID
- 5293
- Acronym
- OCTAPUS-AI
- Status last verified
- Jun 2022
- First submitted
- 6 Nov 2020
- Last updated
- 29 Jun 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Optimising Cancer Therapy And Identifying Causes of Pneumonitis USing Artificial Intelligence
- ClinicalTrials.gov ID
- NCT04721444
- Acronym / sponsor ID
- OCTAPUS-AI / 5293
- Conditions
- Lung Cancer, Covid19, Pneumonitis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,211 participants (actual)
- Lead sponsor
- Royal Marsden NHS Foundation TrustUniversity, hospital or other
- Study start
- 27 Jan 2021 (actual)
- Primary completion
- 1 Mar 2022 (actual)
- Study completion
- 1 Mar 2022 (actual)
- First posted
- 22 Jan 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Distinguishing changes on patients that have received thoracic radiotherapy and patients that are currently receiving or have recently received IO and presenting lung changes which will be identified using AI.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 7
| Arm | Type | Receives |
|---|---|---|
| Arm A - Cohort A1 | – | Diagnostic Test: Machine Learning Classification of parenchymal lung change causeTraining set: Pneumonitis in the context of IO therapy and negative for infectious pneumonia (including COVID-19) |
| Arms A and B - Cohort B1 | – | Diagnostic Test: Machine Learning Classification of parenchymal lung change causeTraining set B1: IO and RT naive and pneumonia (without COVID-19) |
| Arms A and B - Cohort B2 | – | Diagnostic Test: Machine Learning Classification of parenchymal lung change causeTraining set B2: IO and RT naive and confirmed COVID-19 positive with pneumonia |
| Arm B - Cohort A2 | – | Diagnostic Test: Machine Learning Classification of parenchymal lung change causeTraining set: Pneumonitis in the context of thoracic RT and negative for infectious pneumonia (including COVID-19) |
| Arm A - Test Cohort (Cohort C1) | – | Diagnostic Test: Machine Learning Classification of parenchymal lung change causeTest set C1: Patients on IO and with possible toxicity versus COVID-19 or other infective pneumonitis |
| Arm B - Test Cohort (Cohort C2) | – | Diagnostic Test: Machine Learning Classification of parenchymal lung change causeTest set C2: Patients with pneumonitis in context of thoracic RT with possible toxicity versus COVID-19 or other infective pneumonitis. |
| Arm C | – | Diagnostic Test: Machine Learning Classification of recurrence and non-recurrencePatients with radiotherapy planning CT scans and post-treatment surveillance CT scans at 3, 6 and 12-months post treatment |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Arms A and B: Adult patients with pneumonitis that have previously been treated with thoracic radiotherapy or immunotherapy Arm C: Adult patients that have received radical radiotherapy for NSCLC
Key inclusion criteria (29 listed)
- Cohort A1 (from Arm A) - Immunotherapy (IO) pneumonitis cases: patients currently on or having received ICI IO in the last 3 months of presentation with:
- New radiological lung changes on CT/CXR (confirmed on report) of a severity and distribution consistent with IO pneumonitis. These changes should be of severity and distribution that are not incompatible with viral or lower respiratory tract infection.
- AND Must not have had RT involving the thorax (unless this was breast/chest wall RT more than 5 years ago, which is permissible) AND
- Where there is documented clinical concern for infection, have undergone one or more laboratory investigations for viral or lower respiratory tract infection including, but not limited to Nasopharyngeal aspirate or swab for respiratory virus by PCR; Sputum sample or bronchial washings MCS with no organism(s) consistent with lower respiratory tract infection, cytology or beta-glucan/galactomannan for PCP or fungal infection; broncho-alveolar lavage for markers of infection such as MCS, PCR, fungal culture, beta-glucan/galactomannan for PCP or evidence of lower respiratory tract infection (including invasive fungal infection) by cytology, none of which were considered positive for infection by the clinical team.
- Where empirical antibiotics were prescribed, patients must either have had a negative BAL infection screen or may be included at the discretion of the local site PI and local radiologist with lung interest or two members of the trial management group, one of whom must be a radiologist with lung interest or respiratory physician or oncologist with suitable experience of thoracic CT imaging, after after review of the case-notes.
Key exclusion criteria (6 listed)
- Patients with documented past medical history of congestive cardiac failure or other cause for interstitial lung disease
- Any patient that does not have a primary lung mass e.g. Tx disease
- Any patient being treated for recurrence of a previously treated lung cancer
- Any patient that did not have radical RT e.g. patients that had high dose palliative RT
- Any patient that does not have imaging that meets technical requirements within the imaging processing and analysis manual
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine Learning Classification of parenchymal lung change causeArms A \& B: Radiomics and deep-learning approaches will be used on patient's imaging to develop a feature vector that can distinguish parenchymal lung changes, e.g. infection from drug-toxicity. | Diagnostic test | Arm A - Cohort A1, Arm A - Test Cohort (Cohort C1), Arm B - Cohort A2, Arm B - Test Cohort (Cohort C2), Arms A and B - Cohort B1, Arms A and B - Cohort B2 |
| Machine Learning Classification of recurrence and non-recurrenceArm C: Radiomics and deep-learning approaches will be used on patient's imaging to develop a risk-signature for recurrence of malignancy following radical treatment | Diagnostic test | Arm C |
Primary outcome
Development of a Machine Learning model to distinguish parenchymal lung changes
Time frame: 3 years
The development and validation of an ML/radiomic classifier to distinguish between Infective/COVID-19 pneumonia and cancer therapy induced lung changes
Development of a Machine Learning model to predict recurrence risk after radical radiotherapy for non-small cell lung cancer
Time frame: 3 years
to develop a prognostic AI/radiomic signature for NSCLC recurrence after radical RT (Conventional fractionated RT +/- chemotherapy or stereotactic body RT (SBRT)) to stratify appropriate surveillance and onward care, thus minimising unnecessary hospital visits and resource use.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2022.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Richard Lee principal investigator
Royal Marsden NHS Foundation Trust
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Covid19, run by Royal Marsden NHS Foundation Trust at 3 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 29 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Jun 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of a Machine Learning model to distinguish parenchymal lung changes (time frame: 3 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jun 2022.