2.3 Imaging segmentation
We defined three regions of interest (ROIs) in CT images, including
gross target volume (GTV), pelvis,
and sacral vertebrae (Figure 1).
GTV
consisting of the visible primary tumor and positive pelvic lymph node
was previously defined for radiotherapy planning based on both CT and
MRI (contrast-enhanced T1-weighted images, T2-weighted images, DWI
images) by the consensus of experienced radiation oncologists and
radiologists. The size and morphology of all visible lymph nodes were
inspected on CT or MRI images by two oncologists (10 years of
experience) and reviewed by one radiologist (30 years of experience).
A positive lymph node was a
rounded soft-tissue structure with a short-axis diameter >
10mm or with central necrosis [27, 28]. The pelvis and sacral
vertebrae were contoured independently
by two radiologists (10 years of
experience) and confirmed by a radiologist (30 years of experience).
We used radiomic features of GTV
to predict treatment outcomes, while those of
pelvis and sacral vertebrae to
predict hematologic toxicities. In
addition, one radiologist (7 years of experience) independently
contoured GTVs for 30 of 257 CT cases to test the inter-rater
reliability of radiomic features.
2.4 Assessment of treatment outcomes
and hematologic toxicities
The short-term tumor response was assessed based on pelvic MRI
examination 3 months after radiotherapy according to the
Response Evaluation Criteria In
Solid Tumors v. 1.1 [29]: (1) CR represents the disappearance of all
cervical lesions; (2) partial response (PR) represents at least a 30%
decrease in the longest tumor diameter; (3) progressive disease (PD)
represents at least a ≥ 20% increase in the longest diameter of tumor;
and (4) stable disease (SD) represents neither sufficient decrease to
qualify for PR nor sufficient increase in longest diameter for PD. OS
was defined as the time from the date of diagnosis until death or the
last follow-up. Hematologic toxicities during chemoradiation were
assessed according to the National Cancer Institute Common Terminology
Criteria for Adverse Events CTCAE 3.0 [29].
We investigated four clinical endpoints. (1) we categorized patients
into the CR group and the non-CR group to predict tumor regression. (2)
patients were divided into two groups by the cutoff OS time of 5 years.
Binary classification models were used to predict whether patients
survived ≥ 5 years. (3) we predicted whether patients suffered from
severe leucopenia (grade ≥ 3) and (4) hypohemoglobin (grade
> 0) that could reflect treatment tolerance and influence
outcome. Table 2 shows the number of patients stratified by the clinical
endpoints.
Additionally, we used one-class learning algorithm to identify patients
at high risk of treatment failures (SD and PD) due to the small sample
size (5 SD and 4 PD of 257 patients). Methods and results are in
supplementary materials.