Clinical trial NCT05375591: AI & Radiomics for Stratification of Lung Nodules After Radically Treated Cancer
Official title: Artificial Intelligence & Radiomics for Stratification Of Lung Nodules After Radically Treated Cancer (AI-SONAR)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Indeterminate Pulmonary Nodules
Lung Metastases
Second Primary Cancer
Lung Cancer
Lead sponsor: Royal Marsden NHS Foundation Trust
Sponsor class: OTHER
Collaborators: name: Institute of Cancer Research, United Kingdom
name: National Institute for Health Research, United Kingdom
name: Royal Brompton & Harefield NHS Foundation Trust
name: Royal Marsden Partners Cancer Alliance
name: Imperial College London
name: Oxford University Hospitals NHS Trust
name: National Heart and Lung Institute
First posted: 2022-05-16
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Retrospective cohorts of patient cases with a new pulmonary nodule finding on thoracic CT imaging and previous diagnosis of cancer (treated radically) within the past 10 years (or less).

Brief summary: This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer.

Detailed description: Improvements in cancer detection and diagnosis have led to increasing numbers of patients being diagnosed with early stage cancer and potentially receiving curative therapy with improved survival outcomes. Recent retrospective studies in cancer survivors have demonstrated such patients possess an increased risk of further cancer in their lifetime compared to the general population, in part potentially due to shared lifestyle risk factors (e.g. smoking), genetic cancer pre-disposition or downstream oncogenic side effects of anti-cancer therapies (eg. radiotherapy). Lung cancer remains the leading cause of cancer related deaths worldwide and the lungs also represent a common site for metastatic disease in patients with non-pulmonary malignancy. Furthermore, lung cancer is one of the most common second primary malignancy in patients with a prior history of treated cancer. Therefore, discerning the significance of a pulmonary nodule in the context of a previous cancer remains a clinical challenge given it may possess the potential to represent benign disease, metastatic relapse or new primary malignancy.

This study will assess the utility of radiomics and artificial intelligence approaches to new lung nodules in patients who have undergone radical treatment for a previous cancer. This will entail use of machine learning (ML) approaches and later, exploration of deep-learning/convolutional neural network approaches to nodule interpretation for differentiation of benign, metastatic and new primary lung cancer nodules/lesions. Development of a ML classifier or deep learning based tool may help guide which patients would benefit from earlier investigations including additional imaging, biopsy sampling and lead to earlier cancer diagnosis, leading to better patient outcomes in this unique cohort. This is a retrospective study analysing data already collected routinely as part of patient care. All data will be anonymised prior to any analysis, no patient directed/related interventions will be employed and consent-waiver for study inclusion will be exercised.

Eligibility criteria: Inclusion Criteria:

* Confirmed history of previous radically or curative-intent treated solid organ cancer within 10 years of new index CT thoracic scan demonstrating a new pulmonary nodule and either of the following:

  * Biopsy confirming previous malignancy with MDT consensus and successful cancer resolution/remission following anti-cancer treatment on interval imaging or blood assay analysis
  * Where biopsy was not possible/confirmed for previous malignancy, MDT consensus outcome confirming cancer (+/- calculated Herder score \>80% if applicable) and decision to treat as malignancy with subsequent resolution/remission following anti-cancer treatment on interval imaging or blood assay analysis
* Radical treatment for previous cancer defined as either of the following:

  * Surgical resection
  * Radical radiotherapy or stereotactic beam radiotherapy
  * Radical chemotherapy
  * Radical chemo-radiotherapy
  * Multi-modality treatment with any of the above
* New pulmonary nodule ground truth known

  * Scan data showing 2-year stability (based on diameter or volumetry) or resolution in cases of benign disease
  * Scan data showing progressive nodule enlargement or increase in nodule number on interval imaging with MDT consensus (+/- PET with Herder score \>80% if applicable) determining metastatic disease or new primary malignancy
  * Biopsy sampling confirming benign disease or malignancy and in cases of malignancy, metastasis or new primary lung cancer
* CT scan slice thickness ≤ 2.5mm
* Nodule size ≥ 5mm

Exclusion Criteria:

* CT Imaging \> 10 years old
* Non-solid haematological malignancies including leukaemia
* Cases of radically treated primary cancer disease with early oligometastatic recurrence treated radically

Primary outcomes:
measure: Development of a CT-thorax based radiomics ML classifier model to predict cancer risk in new lung nodules after previous radically treated cancer.
timeFrame: 2 years
measure: Development of the CT-thorax based ML classifier model to predict whether a new malignant nodule represents metastatic lung disease (new cancer vs previous cancer recurrence) or a new primary lung malignancy.
timeFrame: 2 years
Secondary outcomes:
measure: To evaluate performance the developed CT-thorax based ML classifier model in an independent external validation cohort.
timeFrame: 2 years
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: The Royal Marsden NHS Foundation Trust (Chelsea Site)
city: London
country: United Kingdom
facility: Royal Brompton Hospital
Study officials:
affiliation: The Royal Marsden Hospitals NHS Trust
name: Richard Lee
role: PRINCIPAL_INVESTIGATOR
References:
citation: Tabuchi T, Ito Y, Ioka A, Miyashiro I, Tsukuma H. Incidence of metachronous second primary cancers in Osaka, Japan: update of analyses using population-based cancer registry data. Cancer Sci. 2012 Jun;103(6):1111-20. doi: 10.1111/j.1349-7006.2012.02254.x. Epub 2012 Apr 11.
citation: Youlden DR, Baade PD. The relative risk of second primary cancers in Queensland, Australia: a retrospective cohort study. BMC Cancer. 2011 Feb 23;11:83. doi: 10.1186/1471-2407-11-83.
citation: Stella GM, Kolling S, Benvenuti S, Bortolotto C. Lung-Seeking Metastases. Cancers (Basel). 2019 Jul 19;11(7):1010. doi: 10.3390/cancers11071010.
citation: Deng L, Harethardottir H, Song H, Xiao Z, Jiang C, Wang Q, Valdimarsdottir U, Cheng H, Loo BW, Lu D. Mortality of lung cancer as a second primary malignancy: A population-based cohort study. Cancer Med. 2019 Jun;8(6):3269-3277. doi: 10.1002/cam4.2172. Epub 2019 Apr 16.
citation: Mery CM, Pappas AN, Bueno R, Mentzer SJ, Lukanich JM, Sugarbaker DJ, Jaklitsch MT. Relationship between a history of antecedent cancer and the probability of malignancy for a solitary pulmonary nodule. Chest. 2004 Jun;125(6):2175-81. doi: 10.1378/chest.125.6.2175.
citation: Johnson BE. Second lung cancers in patients after treatment for an initial lung cancer. J Natl Cancer Inst. 1998 Sep 16;90(18):1335-45. doi: 10.1093/jnci/90.18.1335.
citation: Travis LB. The epidemiology of second primary cancers. Cancer Epidemiol Biomarkers Prev. 2006 Nov;15(11):2020-6. doi: 10.1158/1055-9965.EPI-06-0414. Epub 2006 Oct 20.
citation: Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res. 2017 Feb;6(1):86-91. doi: 10.21037/tlcr.2017.01.04.
citation: Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-2019-214104. Epub 2020 Mar 5.