Clinically significant changes in patients’ anatomy during fractionated radiotherapy, e.g., body shrinkage due to weight loss or target/organ shape and position variations, cause the actual dose delivered to patients to varying considerably different from the originally planned. Adaptive radiotherapy (ART) is a method to account for those changes and adjust the dose
[1]
-
[8]
. However, the process of ART is time-consuming and may not benefit those patients whose anatomy has limited variations
[9]
-
[12]
. Therefore, a quantitative method that considers anatomical changes to predict target/organ dosimetric changes for initiating adaptive planning decisions is highly desirable. This study focuses on the patients’ anatomical changes in predicting parotid dosimetric changes for head-and-neck cancer since xerostomia caused by radiation side effects to the parotid is a major cause of reduced quality of life after radiotherapy
[13]
-
[15]
.
This study hypothesizes that the following three anatomical changes during treatment sessions cause changes in parotid dosimetry: (1) parotid volumetric shrinkage, (2) distance changes from the parotid to the PTV, e.g., displacement of the parotid into (or away from) the high dose region, and (3) length of beam path changes between the parotid and skin, caused by patient’s weight loss near the neck. Anatomical changes in (1) is well-documented
[12]
[16]
-
[20]
. For example, our previous study involving 185 CT scans (including a planning CT and several weekly follow-up CTs) of 26 patients shows that on average the parotid has a volumetric reduction of ~10 cc (or ~30% in terms of the fractional volume) by the 7th week
[16]
[18]
. However, the anatomical changes in hypotheses (2) and (3) have not been studied due to a lack of geometrical features for quantifying the distance and beam path length. Therefore, this study has two aims:
2. Methods2.1. Geometrical Feature Quantification
Signed Euclidean distance
[21]
-
[23]
between two objects (main object and voxelized, secondary object as shown in
Figure 1
) is applied to quantify the distance and beam path length in hypotheses (2) and (3). As illustrated in
Figure 1
, the shortest Euclidean distance d(i, j) from voxel (i, j) of the secondary object to the main object’s surface is calculated: if voxel (i, j) is inside the main object, d(i, j) < 0; if voxel (i, j) is on the surface of the main object, d(i, j) = 0; if voxel (i, j) is outside the main object, d(i, j) > 0. The distance calculation is repeated for every secondary object’s voxel. Finally, a cumulative histogram v = Hist (d) relating the shortest Euclidean distance d and the secondary object’s absolute volume v is formed. v is then normalized by the absolute volume of the secondary object as the fractional volume in the range of [0, 100%]. Hereinafter v refers to the fractional volume.
<xref ref-type="bibr" rid="scirp.137458-"></xref>Figure 1. d(i, j) is the shortest Euclidean distance from the secondary object voxel (i, j) to the main object’s surface. v = Hist (d) is a cumulative histogram in relating distance d and fractional volume v; d<sub>v</sub> = Hist<sup>−</sup><sup>1 </sup>(v) is the inverse function of v = Hist (d), representing distance d at fractional volume v.
Next, we define dv = Hist−1 (v) as the inverse function of v = Hist (d), where dv represents distance d at fractional volume v. For example, d0, d50, and d100 represent the minimal, median and maximal distance from the secondary object to the main object’s surface;
represents the mean distance from the secondary object to the main object’s surface, where N is the total number of voxels inside the secondary object. Finally, dv and dmean are applied to quantify the distance and beam path length in hypotheses (2) and (3), where the parotid is the secondary object and the PTV and skin are the main objects.
65 geometrical features categorized into four sets in
Table 1
are defined a priori for quantifying patients’ anatomical changes and predicting parotid dosimetric changes:
In
Table 1
, distance features dmean and dv are calculated from dv = Hist−1 (v) (inverse function of v = Hist(d) defined in
Figure 1
), where the parotid is the secondary object, where PTV, skin and bony landmarks are the main objects.
SET4: distance between the parotid and two bony landmarks* (cm)
volume in cc
dmean and {dv}#
dmean and {dv}#
dmean and {dv}#
Feature No.: 1
Feature No.: 2 - 17
Feature No.: 18 - 33
Feature No.: 34 - 65
#v as the fractional volume in %: 0, 0.5, 1.0, 2.5, 5, 20, 35, 50, 65, 80, 95, 97.5, 99, 99.5 and 100. *: surrogates for the PTV in SET2; Bone1 landmark: the dens of C2; Bone2 landmark: the tip of the basilar part of the occipital bone. See
Figure 2
for the details.
Figure 2. Two bony landmarks (Bone1 and Bone2; coronal view) as surrogates of the PTV in SET 2 for quantifying the position of the parotid relevant to the high dose region.
As discussed in Section 2.2, the two introduced bony landmarks in SET4 are used as surrogates for the PTV in SET2 to determine the parotid’s position with respect to the high-dose region.
Figure 3
provides a simplified geometrical illustration of the four geometric sets.
2.2. Study Design
Eighteen patients with head-and-neck cancer previously treated with ART are retrospectively enrolled. Each patient has two CTs: initial planning CT (initialCT) and adaptive planning CT (adaptiveCT), which are acquired in the middle of the treatment. Physicians contour respective targets and organs in both CTs. Each patient has two VMAT plans: Plan1 is the initial plan based on initialCT with its associated contours; Plan2 is the forward-dose calculation plan, applying Plan1’s beam configuration on the adaptiveCT with its associated contours. In plan evaluation, our institution uses the mean dose to the parotid as the primary dosimetric metric. Therefore, this study focuses on predicting parotid mean dose changes.
Figure 3. Simplified illustration of the 4-SET geometrical features in predicting parotid dosimetric changes. SET 1: the parotid absolute volume in cc; SET 2: the distance of the parotid to the PTV; SET 3: the distance of the parotid to the skin near the neck area; SET 4: the distance of the parotid to the two bony landmarks (Bone1 and Bone2) shown in Figure 2. Bone1: the dens of C2; Bone2: the tip of the basilar part of the occipital bone.
The two bony landmarks introduced in SET4 of
Table 1
are used as surrogates of the PTV in SET2 for the following reason: in this retrospective study, the PTV contours in adaptiveCT (or Plan2) are available since all enrolled patients have been treated using ART. However, in clinical application, the PTV contours in adaptiveCT may not be available since target contouring is time-consuming and not always completed until a decision to use ART is made
[11]
. Since the two bony landmarks in SET4 are rigid, both provide localization reference shared by initialCT and adaptiveCT. Additionally, they are easily contoured as only two slices are needed in the transverse plane. Thus, the introduced bony landmarks allow us to investigate whether the parotid is being pushed into (or away from) the high dose region without using PTV contours in initialCT and adaptiveCT.
For each enrolled patient, corresponding DICOM RT-dose, RT-structure and CTs of Plan1 and Plan2 are extracted from the treatment planning system. An in-house developed Matlab (The MathWorks, Inc., Natick, MA, US) program is used to calculate
Table 1
’s described features from the DICOM data. Below, we define the anatomical feature changes between the initialCT (Plan1) and adaptiveCT (Plan2) as:
where “| |” is the absolute magnitude. A positive ratio means Plan2’s feature (volume or distance) is smaller than Plan1. Next, we define the parotid mean dose changes as the ratio of the relative parotid mean dose of Plan1 to Plan2:
Dose ratio = (Plan1’s relative mean dose in %)/(Plan2’s relative mean dose in %) (2)
where “%” is the organ’s absolute mean dose normalized by the target prescription. Dose ratio < 1.0 means that the Plan2’s relative mean dose in % is larger than Plan1. The introduced relative mean dose in % allows us to compare the parotid dose from the plans with the different target prescriptions.
2.3. A Machine-Learning Approach
A decision tree classifier for predicting x% parotid mean dose increase over Plan1 is built from the data of the 36 parotids of the 18 enrolled patients. The predictors are the 65 anatomical ratios calculated from Equation (1) using the features in
Table 1
. The response is the binarized dose ratio calculated from Equation (2): if the parotid’s relative mean dose in % from Plan2 is x% larger than its value in Plan1, the response is “1”; otherwise, it is “0”. x is set at 0%, 5%, 10%, 15% and 20% to generate the corresponding binary response. Since only the data of the 36 parotids are available, leave-one-out cross-validation is applied in model training and verification. Additionally, in the feature selection, enumerating k-combination (k = 1, 2, 3 and 4) of the 65 predictors combined with the leave-one-out cross-validation is used to find a feature subset maximizing the classifier’s accuracy under various x% parotid mean dose increase. Gini-diversity index and maximal number of decision splits = 10 are used in the decision tree algorithm. Note that in this retrospective study, SET2’s PTV features from adaptiveCT (Plan2) are available, while in clinical application, those PTV features would not. Therefore, SET4 with the two bony landmarks are introduced as surrogates for the SET2’s PTV features. Accordingly, the classifier’s accuracy with/without SET2’s PTV features is evaluated to determine the feasibility of the surrogates.
3. Results
Figure 4
shows the found feature subset yielding maximal predicting accuracy with/without SET2 (e.g., PTV features from the 2nd to 17th in
Table 1
) under various x% parotid mean dose increases. The results show that when SET2’s PTV features are excluded from feature selection, the accuracy drops by ~5.5% to 86.1% in x = 0%, 5% and 10% cases. In x = 15% case, SET2’s PTV features inclusion does not improve accuracy. In x = 20% case, SET2’s PTV features inclusion improves accuracy by ~2.7% to 97.2%. In addition,
Figure 4
shows that the parotid volumetric change (SET1) is an irrelevant feature in x = 5%, 10%, 15% and 20% cases.
Table 2
illustrates the confusion matrix and predicting accuracy for various x% when SET2’s PTV features are excluded. The last column shows the improvement of the accuracy over prevalence (maximal prior probability of true class of the 32 parotids in this study).
Figure 5
shows an example of the decision boundary of a classifier for predicting x = 10% parotid mean dose increase, excluding SET2’s PTV features in the feature selection: feature 28th (d80) in SET3 and feature 57th (d35) in SET4 yield a maximal accuracy of 86.1% with 19.4% improvement over prevalence = 66.7%; confusion matrix parameters are: TP = 9, FP = 3, FN = 3, TN = 22, TPR = 75%, TNR = 91.7%, PPV = 81.8% and NPV = 88%. The definitions of confusion matrix and TP, FP, FN, TPR, TNR, PPV and NPV are discussed in Wikipedia
[24]
.
Figure 4. The found feature subset yielding maximal predicting accuracy under various x% parotid mean dose increases. “Red circle”: feature selection excluding SET 2 (PTV features) of <xref ref-type="table" rid="table1">
Table 1
</xref>; “Blue diamond”: feature selection including all SETs.
<xref ref-type="bibr" rid="scirp.137458-"></xref>Table 2. Classifier performance evaluation excluding SET2’s PTV features in feature selection: confusion matrix, predicting accuracy and prevalence.
x%: dose increase
True class
Predicted class
Predicting accuracy
Prevalence: maximal prior probability of true class
Improvement #
“0”
“1”
“1”: >x = 0%&
“0”
11 (73.3%)
4 (26.6%)
86.1%
True class “1”:
(20 + 1)/36 = 58.3%
27.8%
“1”
1 (4.7%)
20 (95.23%)
“1”: >x = 5%
“0”
17 (85%)
3 (15%)
86.1%
True class “0”:
(17 + 3)/36 = 55.6%
30.5%
“1”
2 (12.5%)
14 (87.5%)
“1”: >x = 10%
“0”
22 (91.7%)
2 (8.3%)
86.1%
True class “0”:
(22 + 2)/36 = 66.7%
19.4%
“1”
3 (25%)
9 (75%)
“1”: >x = 15%
“0”
31 (100%)
0 (0%)
91.7%
True class “0”:
(31 + 0)/36 = 86.1%
5.5%
“1”
3 (60%)
2 (40%)
“1”: >x = 20%
“0”
32 (100%)
0 (0%)
94.4%
True class “0”:
(32 + 0)/36 = 88.9%
5.5%
“1”
2 (50%)
2 (50%)
&Class “1”: parotid mean dose increase > x%; #: difference between accuracy and prevalence. Prevalence: maximal prior probability of true class of the 32 parotids in this study.
Figure 5. The decision boundary of a classifier in predicting x = 10% parotid mean dose increase, excluding SET2’s PTV features in the feature selection. Feature 57<sup>th</sup> in SET4 from the bony landmarks and feature 28<sup>th</sup> in SET3 from the skin features are found to yield maximal accuracy of 86.1%. “1”: means parotid mean dose increase is larger than 10%.
4. Discussion
This study proposes three hypotheses related to anatomical changes in predicting parotid mean dose increase in head-and-neck fractioned radiotherapy. Surprisingly, Hypothesis 1, parotid volumetric changes (Feature 1 in
Table 1
), is found not to be among the top three features in predicting parotid mean dose increase, as shown in
Figure 4
. Both Hypotheses 2 (the distance changes from the parotid to the PTV) and 3 (the length of beam path changes between the parotid and skin near the neck) are found to be strong predictors.
Before discussing the findings in detail, we identified the following limits: 1) We also applied two other machine learning methods, Support Vector Machine and Ensemble Methods, for predicting parotid dose changes. However, those methods generate inferior results to the proposed decision tree method, and it is not worth discussing. We do not apply Neural Network approach since there is not enough patient data for training and testing. 2) The clinical decisions for adaptive planning of the 18 enrolled patients in this study vary (e.g., mask unfitting due to weight loss, or tumor size variations observed from on-board imaging): none of the adaptive planning decisions are triggered due to parotid dose concerns. 3) The 4-SET geometrical features, as shown in
Table 1
, are pre-defined, which could limit the searching space of the feature selection and performance of the classifier. 4) The goal of the study is to find a subset of the proposed geometric features in predicting parotid dose changes. How to clinically incorporate those findings in adaptive planning decisions is beyond the study.
However, the confusion matrix in
Table 2
gives some hints in implementing the findings in clinics:
To our knowledge, the findings in Hypothesis 2 are the first to use the bony landmarks shown in
Figure 2
as surrogates for the PTV in quantifying the motion of the parotid relative to the high dose region. Several studies show that the parotid tends to shift medially towards the PTV areas during treatment, potentially jeopardizing parotid sparing
[12]
[16]
-
[20]
. Since target and organ contours in both initial and adaptive CTs are available in this study, the classifier performance using PTV features (or SET2 in
Table 1
) is evaluated as a benchmark. We find that without PTV feature inclusion, the classifier’s accuracy at x = 5% and x = 10% decreases by only 5.6% from the 91.7% benchmark, demonstrating that the proposed bony landmarks are feasible surrogates for the PTV. In addition,
Figure 5
shows that 9 out of 11 parotids predicting as “1”, e.g., parotid’s mean dose increased by 10%, have the reduced distance (e.g., Feature 57’s anatomic ratio in Equation (1) being positive) from the parotid to the bony landmarks. Since the bony landmarks are the rigid localization reference between initial and adaptive CTs, our findings indicate that parotids being shifted closer to the bony landmarks (e.g., reduced distance as mentioned above) would receive higher dose during treatment sessions, which is in line with the reports using PTV
[12]
[16]
-
[20]
.
The benefits of not having to include the PTV contours in predicting parotid mean dose increase are obvious: in clinical application, target contouring is time-consuming and generally not performed for the adaptive CT (or Plan2) unless the decisions to adapt planning have been made. However, the bony landmark surrogates introduced here only require 2-slice contours in the transverse plane, which can be easily segmented by therapists/dosimetrists/physicists. How to incorporate those segmented bony landmarks as surrogates of PTV in adaptive planning decisions is our future research.
Another factor influencing the parotid’s mean dose is weight loss during treatment sessions. De Bari et al. found that external body contour changes could serve as a flag to trigger adaptive planning
[25]
-
[27]
. Our study focuses on the skin contour near the neck.
Figure 5
shows that Feature 28 quantifying the beam path length between the skin and parotid (SET3 in
Table 1
) is a key feature in predicting parotid mean dose increase: 9 out of 11 parotids predicted as “1”, e.g., parotid’s mean dose increased by 10%, have an enlarged distance (e.g., Feature 28’s anatomic ratio in Equation (1) being negative) from the skin to the parotid. At present, we are not able to explain the above observed correlation between enlarged distance and parotid mean dose increase. Future research is necessary to investigate the issue.
The findings in Hypothesis 1 do not fully align with the published literature
[12]
[16]
-
[20]
: we do not find parotid shrinkage (or volumetric changes in general) to be a key factor in correlating with parotid dose increase. Indeed, our study finds that parotid medially shift towards the PTV areas (or the proposed bony landmarks as PTV surrogates) is a key factor in predicting parotid dose increase during treatment sessions. Further study is necessary in this direction.
5. Conclusion
We have presented a machine-learning approach that utilizes a decision-tree classifier to predict parotid mean dose changes caused by patients’ anatomical changes in fractionated radiotherapy for head-and-neck cancer. In the current 18 enrolled patients, we found that if parotid mean dose increase is a major concern, the contours of the parotid, skin near the neck area and proposed bony landmarks in initial and adaptive CTs are sufficient predictors. Further study with a larger sample size of patients is necessary to provide a robust basis for generalization.
References
Chen, A.M., Daly, M.E., Cui, J., Mathai, M., Benedict, S. and Purdy, J.A. (2014) Clinical Outcomes among Patients with Head and Neck Cancer Treated by Intensity-Modulated Radiotherapy with and without Adaptive Replanning. Head&Neck, 36, 1541-1546. >https://doi.org/10.1002/hed.23477
Schwartz, D.L., Garden, A.S., Thomas, J., Chen, Y., Zhang, Y., Lewin, J., et al. (2012) Adaptive Radiotherapy for Head-and-Neck Cancer: Initial Clinical Outcomes from a Prospective Trial. International Journal of Radiation, Oncology, Biology, Physics, 83, 986-993. >https://doi.org/10.1016/j.ijrobp.2011.08.017
Yang, H., Hu, W., Wang, W., Chen, P., Ding, W. and Luo, W. (2013) Replanning during Intensity Modulated Radiation Therapy Improved Quality of Life in Patients with Nasopharyngeal Carcinoma. International Journal of Radiation, Oncology, Biology, Physics, 85, e47-e54. >https://doi.org/10.1016/j.ijrobp.2012.09.033
Dawson, L.A. and Sharpe, M.B. (2006) Image-Guided Radiotherapy: Rationale, Benefits, and Limitations. The Lancet Oncology, 7, 848-858. >https://doi.org/10.1016/s1470-2045(06)70904-4
Castadot, P., Geets, X., Lee, J.A. and Grégoire, V. (2011) Adaptive Functional Image-Guided IMRT in Pharyngo-Laryngeal Squamous Cell Carcinoma: Is the Gain in Dose Distribution Worth the Effort? Radiotherapy and Oncology, 101, 343-350. >https://doi.org/10.1016/j.radonc.2011.06.011
Brouwer, C.L., Steenbakkers, R.J.H.M., Langendijk, J.A. and Sijtsema, N.M. (2015) Identifying Patients Who May Benefit from Adaptive Radiotherapy: Does the Literature on Anatomic and Dosimetric Changes in Head and Neck Organs at Risk during Radiotherapy Provide Information to Help? Radiotherapy and Oncology, 115, 285-294. >https://doi.org/10.1016/j.radonc.2015.05.018
Stoll, M., Giske, K., Debus, J., Bendl, R. and Stoiber, E.M. (2014) The Frequency of Re-Planning and Its Variability Dependent on the Modification of the Re-Planning Criteria and IGRT Correction Strategy in Head and Neck IMRT. Radiation Oncology, 9, Article No. 175. >https://doi.org/10.1186/1748-717x-9-175
Ahn, P.H., Chen, C., Ahn, A.I., Hong, L., Scripes, P.G., Shen, J., et al. (2011) Adaptive Planning in Intensity-Modulated Radiation Therapy for Head and Neck Cancers: Single-Institution Experience and Clinical Implications. International Journal of Radiation, Oncology, Biology, Physics, 80, 677-685. >https://doi.org/10.1016/j.ijrobp.2010.03.014
Huang, H., Lu, H., Feng, G., Jiang, H., Chen, J., Cheng, J., et al. (2015) Determining Appropriate Timing of Adaptive Radiation Therapy for Nasopharyngeal Carcinoma during Intensity-Modulated Radiation Therapy. Radiation Oncology, 10, Article No. 192. >https://doi.org/10.1186/s13014-015-0498-1
Brown, E., Owen, R., Harden, F., Mengersen, K., Oestreich, K., Houghton, W., et al. (2015) Predicting the Need for Adaptive Radiotherapy in Head and Neck Cancer. Radiotherapy and Oncology, 116, 57-63. >https://doi.org/10.1016/j.radonc.2015.06.025
You, S.H., Kim, S.Y., Lee, C.G., Keum, K.C., Kim, J.H., Lee, I.J., et al. (2012) Is There a Clinical Benefit to Adaptive Planning during Tomotherapy in Patients with Head and Neck Cancer at Risk for Xerostomia? American Journal of Clinical Oncology, 35, 261-266. >https://doi.org/10.1097/coc.0b013e31820dc092
Zhang, P., Simon, A., Rigaud, B., Castelli, J., Ospina Arango, J.D., Nassef, M., et al. (2016) Optimal Adaptive IMRT Strategy to Spare the Parotid Glands in Oropharyngeal Cancer. Radiotherapy and Oncology, 120, 41-47. >https://doi.org/10.1016/j.radonc.2016.05.028
Harrison, L.B., Zelefsky, M.J., Pfister, D.G., Carper, E., Raben, A., Kraus, D.H., et al. (1997) Detailed Quality of Life Assessment in Patients Treated with Primary Radiotherapy for Squamous Cell Cancer of the Base of the Tongue. Head&Neck, 19, 169-175. >https://doi.org/10.1002/(sici)1097-0347(199705)19:3<169::aid-hed1>3.0.co;2-0
Bjordal, K., Kaasa, S. and Mastekaasa, A. (1994) Quality of Life in Patients Treated for Head and Neck Cancer: A Follow-Up Study 7 to 11 Years after Radiotherapy. International Journal of Radiation, Oncology, Biology, Physics, 28, 847-856. >https://doi.org/10.1016/0360-3016(94)90104-x
Jensen, A.B., Hansen, O., Jørgensen, K. and Bastholt, L. (1994) Influence of Late Side-Effects Upon Daily Life after Radiotherapy for Laryngeal and Pharyngeal Cancer. Acta Oncologica, 33, 487-491. >https://doi.org/10.3109/02841869409083923
Sanguineti, G., Ricchetti, F., Wu, B., McNutt, T. and Fiorino, C. (2015) Parotid Gland Shrinkage during IMRT Predicts the Time to Xerostomia Resolution. Radiation Oncology, 10, Article No. 19. >https://doi.org/10.1186/s13014-015-0331-x
Castelli, J., Simon, A., Rigaud, B., Lafond, C., Chajon, E., Ospina, J.D., et al. (2016) A Nomogram to Predict Parotid Gland Overdose in Head and Neck IMRT. Radiation Oncology, 11, Article No. 79. >https://doi.org/10.1186/s13014-016-0650-6
Ricchetti, F., Wu, B., McNutt, T., Wong, J., Forastiere, A., Marur, S., et al. (2011) Volumetric Change of Selected Organs at Risk during IMRT for Oropharyngeal Cancer. International Journal of Radiation, Oncology, Biology, Physics, 80, 161-168. >https://doi.org/10.1016/j.ijrobp.2010.01.071
Bhide, S.A., Davies, M., Burke, K., McNair, H.A., Hansen, V., Barbachano, Y., et al. (2010) Weekly Volume and Dosimetric Changes during Chemoradiotherapy with Intensity-Modulated Radiation Therapy for Head and Neck Cancer: A Prospective Observational Study. International Journal of Radiation, Oncology, Biology, Physics, 76, 1360-1368. >https://doi.org/10.1016/j.ijrobp.2009.04.005
Barker, J.L., Garden, A.S., Ang, K.K., O'Daniel, J.C., Wang, H., Court, L.E., et al. (2004) Quantification of Volumetric and Geometric Changes Occurring during Fractionated Radiotherapy for Head-and-Neck Cancer Using an Integrated CT/Linear Accelerator System. International Journal of Radiation, Oncology, Biology, Physics, 59, 960-970. >https://doi.org/10.1016/j.ijrobp.2003.12.024
Saito, T. and Toriwaki, J. (1994) New Algorithms for Euclidean Distance Transformation of an N-Dimensional Digitized Picture with Applications. Pattern Recognition, 27, 1551-1565. >https://doi.org/10.1016/0031-3203(94)90133-3
Wu, B., Ricchetti, F., Sanguineti, G., Kazhdan, M., Simari, P., Chuang, M., et al. (2009) Patient Geometry-Driven Information Retrieval for IMRT Treatment Plan Quality Control. Medical Physics, 36, 5497-5505. >https://doi.org/10.1118/1.3253464
Kazhdan, M., Simari, P., McNutt, T., Wu, B., Jacques, R., Chuang, M., et al. (2009) A Shape Relationship Descriptor for Radiation Therapy Planning. In: Lecture Notes in Computer Science, Springer, 100-108. >https://doi.org/10.1007/978-3-642-04271-3_13
https://en.wikipedia.org/wiki/Confusion_matrix
Capelle, L., Mackenzie, M., Field, C., Parliament, M., Ghosh, S. and Scrimger, R. (2012) Adaptive Radiotherapy Using Helical Tomotherapy for Head and Neck Cancer in Definitive and Postoperative Settings: Initial Results. Clinical Oncology, 24, 208-215. >https://doi.org/10.1016/j.clon.2011.11.005
Hansen, E.K., Bucci, M.K., Quivey, J.M., Weinberg, V. and Xia, P. (2006) Repeat CT Imaging and Replanning during the Course of IMRT for Head-And-Neck Cancer. International Journal of Radiation, Oncology, Biology, Physics, 64, 355-362. >https://doi.org/10.1016/j.ijrobp.2005.07.957
De Bari, B., Ait Erraisse, M., Chekrine, T., Rabilloud, M., Shakir Shakir, I., Lebras, L., et al. (2012) Does Weight Loss Predict Accuracy of Setup in Head and Neck Cancer Patients Treated with Intensity-Modulated Radiation Therapy? La radiologia medica, 117, 885-891. >https://doi.org/10.1007/s11547-011-0765-7