Introduction
Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds—urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.
Patients and methods
In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.
Results
The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC,
P
< 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC,
P
= 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (
P
< 0.001), resulting in a 1-year survival difference of 24% (
P
= 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.
Conclusions
These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.
The functional imaging combining ADC and SUVmax in a single analysis permits to detect changes in cellular tissue structures useful for the assessment of tumour response after the neoadjuvant therapy in rectal cancer, increasing the sensitivity in correct depiction of treatment response than either method alone.
BackgroundCheckpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.MethodsA cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients’ follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.ResultsOur results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations.ConclusionsOur results demonstrate that deep learning can quantify tumor- and non–tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.
Magnetic resonance (MR) imaging of the liver is an important tool for the detection and characterization of focal liver lesions and for assessment of diffuse liver disease, having several intrinsic characteristics, represented by high soft tissue contrast, avoidance of ionizing radiation or iodinated contrast media, and more recently, by application of several functional imaging techniques (i.e., diffusion-weighted sequences, hepatobiliary contrast agents, perfusion imaging, magnetic resonance (MR)-elastography, and radiomics analysis). MR functional imaging techniques are extensively used both in routine practice and in the field of clinical and pre-clinical research because, through a qualitative rather than quantitative approach, they can offer valuable information about tumor tissue and tissue architecture, cellular biomarkers related to the hepatocellular functions, or tissue vascularization profiles related to tumor and tissue biology. This kind of approach offers in vivo physiological parameters, capable of evaluating physiological and pathological modifications of tissues, by the analysis of quantitative data that could be used in tumor detection, characterization, treatment selection, and follow-up, in addition to those obtained from standard morphological imaging. In this review we provide an overview of recent advanced techniques in MR for the diagnosis and staging of hepatocellular carcinoma, and their role in the assessment of response treatment evaluation.
New generation CT scanner, using high resolution MPR images, represents a reliable diagnostic tool in assessment of loco-regional and whole body staging of advanced rectal cancer, especially in patients with MRI contraindications.
Institutional review board statement: The study was ethically approved by the San Gerardo Hospital Ethics Committee. Clinical trial registration statement: This study is registered at "San Gerardo Hospital" trial registry. The registration identification number is EP 15673/19.
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