4. Discussion
Radiation resistance is an
independent poor prognostic factor, and adverse reactions affect tumor
response and long-term survival for LACC.
This study integrated
clinicopathological parameters and treatment planning CT-based radiomics
for predicting CR, 5-year OS, and
hematologic toxicities. The clinical and radiomic models outperformed
the single models (validation balanced accuracies: 80.79% vs 70.34%
and 75.24%) in predicting CR, indicating that a hybrid approach may
have greater power for CR. For predicting OS, the radiomic model showed
superior performance (validation balanced accuracy: 85.75%). Although
only using the tumor volume to build SVM models can predict short-term
CR with good performance (validation balanced accuracy: 72.87%), it
showed low predictive power for long-term OS (validation balanced
accuracy: 50%). To predict hematologic toxicities, combining radiomic
features with clinicopathological parameters can achieve
validation balanced accuracies of
69.93% and 70.96% for severe leucopenia and hypohemoglobin,
respectively. Treatment planning
CT radiomics of bone marrow may be potential biomarkers for the
prediction of treatment outcomes and
hematologic toxicities in LACC.
Previous studies have demonstrated that tumor volume, pelvic lymph node
status, and concurrent chemotherapy are independent prognostic factors
of LACC [35]. But Lucia et al. reported that radiomic models have
higher prognostic power than clinicopathological parameters, such as
FIGO stage, tumor volume, and nodal stage [36]. Fang et al.
constructed a multihabitat MRI radiomic model to predict tumor response
with an AUC of 0.8 [37]. Our models integrated the CT radiomic
features with clinicopathological parameters to predict CR achieved an
AUC of 0.87, which outperformed the clinical (AUC 0.75) model, CT
radiomic model (AUC 0.85), and MRI model (AUC 0.80). For predicting
5-year OS, our radiomic model reached an AUC of 0.82, the clinical and
radiomic model only achieved an AUC of 0.81. The predictive power of the
joint model is reduced possibly because different treatment regimens
were performed to 257 patients. After the models were re-trained using
159 patients treated equally, the clinical and radiomic model achieved
better performance than the radiomic model (Table S3). Previously, Ho et
al. and Lucia utilized MRI radiomics to predict disease-free survival
for cervical cancer patients, but the absence of long-term survival led
the both studies to be limited [36, 38].
We predicted both short-term CR
and long-term survival, which help radiation therapists distinguish
radio-resistance candidates in the early stage and adjusted treatment
regimen in time such as the addition of radio-sensitizers and/or more
intensive follow-up.
Bone marrow cells are easily
damaged by low-dose radiation, which may be associated
with hematologic toxicities.
Previous studies have found the correlation between hematologic
toxicities and dose-volume parameters of pelvic bone marrow based on
PET-CT in rectal cancer and gynecological oncology [39, 40].
Utilizing CT radiomics of the
pelvis and sacral vertebrae to predict hematologic toxicities has not
yet been reported. We combined
clinicopathological parameters and CT radiomics to predict
hypohemoglobin (grade > 0) and severe leucopenia (grade ≥
3) with AUCs of 0.74 and 0.64. The decreased hemoglobin levels are
associated with the prognosis of radiotherapy and hypoxia-induced
radio-resistance [41, 42]. Severe leucopenia increases the risk of
infection and radiotherapy often needs to be suspended when it happens,
which negatively affects the therapeutic efficacy. The accurate and
timely prediction of hematologic toxicities may help to avoid serious
complications. Future studies will
take dosimetric factors of pelvis and sacral vertebrae into account to
further improve the predictive power of our models for hematologic
toxicities.
There are some limitations in our study.
(i) Our results were not
externally validated. External and multicentric data are needed to
validate our results. (ii) The radiomic predictors for hematologic
toxicities were extracted from CT images, which cannot fully present
bone function alterations and bone-related diseases. Other imaging
techniques, such as bone mineral densitometry, PET, and multiparametric
MRI, may provide more predictive information for analyzing
radiation-induced hematologic toxicities. (iii) We only studied planning
CT and contrast-enhanced MRI T1-weighted images for predicting treatment
outcomes without considering other CT/MRI sequences and modalities that
may reveal underlying information for prediction. Further research is
needed to investigate multimodality radiomic models and refine the
optimal combination of clinical and imaging radiomic features.
In conclusion, noninvasive models based on clinicopathological
parameters and treatment planning CT radiomics had predictive power for
CR, 5-year OS, and hematologic toxicities. Before clinical application,
external validation with a larger cohort is needed to refine the models
for predicting the risk of treatment failure and hematologic toxicities.