Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Arm A - Cohort A1 | Training set: Pneumonitis in the context of IO therapy and negative for infectious pneumonia (including COVID-19) |
| |
| Arms A and B - Cohort B1 | Training set B1: IO and RT naive and pneumonia (without COVID-19) |
| |
| Arms A and B - Cohort B2 | Training set B2: IO and RT naive and confirmed COVID-19 positive with pneumonia |
| |
| Arm B - Cohort A2 | Training set: Pneumonitis in the context of thoracic RT and negative for infectious pneumonia (including COVID-19) |
| |
| Arm A - Test Cohort (Cohort C1) | Test set C1: Patients on IO and with possible toxicity versus COVID-19 or other infective pneumonitis |
| |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Machine Learning Classification of parenchymal lung change cause | Diagnostic 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | 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. | 3 years |
Not provided
Not provided
Arms A & B:
Inclusion Criteria:
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
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
B1 (Utilised in Arms A & B) Non-COVID-19 infective cases:
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
Exclusion Criteria:
• Patients with documented past medical history of congestive cardiac failure or other cause for interstitial lung disease
Arm C:
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
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
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Richard Lee | Royal Marsden NHS Foundation Trust | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Guys and St. Thomas' NHS Foundation Trust | London | United Kingdom | ||||
| Imperial College Healthcare NHS Trust |
Not provided
Not provided
Not provided
Not provided
| 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. |
|
| Arm C | Patients with radiotherapy planning CT scans and post-treatment surveillance CT scans at 3, 6 and 12-months post treatment |
|
| Machine Learning Classification of recurrence and non-recurrence | Diagnostic 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 |
|
| London |
| United Kingdom |
| Royal Marsden NHS Foundation Trust | London | United Kingdom |
| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| D000086382 | COVID-19 |
| D011014 | Pneumonia |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D011024 | Pneumonia, Viral |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
Not provided
Not provided