Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT05541744 |
| Other study ID # |
IRB111-168-A |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
August 1, 2023 |
| Est. completion date |
July 31, 2028 |
Study information
| Verified date |
September 2022 |
| Source |
Buddhist Tzu Chi General Hospital |
| Contact |
Yu-Hung Chen, M.D. |
| Phone |
886-3-8561825 |
| Email |
jedimasterchen[@]hotmail.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Lung cancer is currently the leading cause of cancer-related mortality worldwide, and the
dominant histopathology is non-small cell lung cancer (NSCLC). Although many new targeted and
immunomodulation therapies have emerged, not all patients are responsive to novel
therapeutics. A more reliable and accurate risk stratification model to predict the treatment
response and survival outcomes are still lacking. The 18F-fluorodeoxyglucose (18F-FDG)
positron emission tomography (PET) derived radiomics can be used to interrogate tumor
biologies such as glycolytic activity and heterogeneity. It can, therefore, be used to
predict treatment response and survival outcomes. Cancer genomics derived from gene
sequencing can evaluate cancer's genetic alterations. It can be used to feature the genotype
of the tumor. However, both tools have drawbacks; combining these two modalities may enable a
more robust predictive model for more precise clinical decisions. During the investigator's
former study project, the investigators published four Science Citation Index journal papers
using the investigators' research results, which found that 18F-FDG PET radiomics can
independently predict regional lymph node metastasis in NSCLC and cancer survival by stage.
The preliminary findings of the investigator's former research project also disclosed an
association between 18F-FDG PET-derived molecular radiomics with genomic heterogeneity and
mutation of specific glucose metabolic genes. This time, the investigators plan to include
deep radiomics in addition to traditional handcrafted radiomics. The investigators aim to
investigate the radiogenomic patterns in different driver gene mutation statuses and clinical
scenarios. Finally, the investigators seek to use radiogenomics as a prognostic
stratification tool in patients with NSCLC.
Description:
This is a prospective study and The investigators use routine pathological specimens for
whole exome sequencing (WES) and immunohistochemical stains.
Pathological examinations include PD-L1, EGFR status, ALK, and ROS-1.
WES： Total DNA were extracted from paraffin-embedded tumor specimens with the QIAamp DNA FFPE
Tissue Kit (QIAGEN GmbH, Hilden, Germany). The coding size was 45 Mb. For DNA whole exome
sequence, briefly, tumors were sonicated by Covaris M220 sonicator (Life Technologies Europe,
Gent, Belgium) and then ligated to adaptor for further amplification (Illumina® TruSeq Exome
Library Prep, USA). After library preparation, all samples were sequenced using the
NextSeq500 system according to the manufacturer's instructions (Illumina, San Diego, USA).
The investigators run sequencing with 12 samples simultaneously (a total of 100 Gb). The
sequence length was 150 bp with a paired-end (2*150bp). The average depth of sequencing is
100X. After sequencing performance, quality of reads file (fastq) was assessed by FastQC and
then mapped using human Hg19 as the reference. Bam files were used as input for the Varscan
algorithm to identify germline and somatic mutations. Variants annotated and filtered are
manually checked using IGV (Integrative Genomics Viewer), then confirmed by Sanger sequence.
The investigators analyze the clinical related gene alterations including actionable gene
mutations (EGFR, BRAF, KRAS, and MET.) Also, clinically important genes including the
mutation status of TP53 and SDH genes are analyzed. The investigators also analyzed the
mutation status of glucose metabolic cluster genes.
TMB (tumor mutation burden) per megabase: The total number of mutations counted is divided by
the size of the coding region of the targeted territory.
MATH (mutant-allele tumor heterogeneity): The investigators first obtain the MAF (the
fraction of DNA that shows the mutated allele at a gene locus) of each tumor specimen. The
MAF distribution will be used to calculate the median (center of distribution) and the MD
(median deviation) of MAFs in a tumor. The MD is determined by obtaining the absolute
differences of all MAFs from the median MAF. Then the median of the absolute differences is
multiplied by a factor of 1.4826 to obtain the MD. The MATH is calculated as the percentage
ratio of the MD to the median: MATH = (MD/median)×100 [45].
Shannon diversity index (Shannon entropy) [50]: The MAF distribution (histogram) of each
patient's tumor specimen was obtained with different bin sizes (total bin size = S). The
Shannon diversity index is then calculated according to the distribution of probabilities of
each MAF bin.
The image features of FDG PET the investigators extracted as follows,
The traditional image parameters include SUVmax, metabolic tumor volume (MTV) and total
lesion glycolysis (TLG) of the primary tumor. The traditional FDG PET parameters are
calculated using commercialized software (PBAS, PMOD 4.0). Radiomics (texture analysis) will
be calculated only for pre-treatment FDG PET. The matrices of radiomic analysis include
histogram analysis, Gray-level co-occurrence matrix (GLCM), gray-level run-length matrix
(GLRLM), gray-level size zone matrix (GLSZM), neighborhood gray-tone difference matrix
(NGTDM), and shape features.
The investigators put the segmented volume into convolutional neural network
(CNN) for analysis. The investigators will use supervised CNN to analyze the relationship
between imaging with other outcomes.
