Radiomics in surgical oncology: applications and challenges

Figures & data

Figure 1. This graphic gives a patient a top-level view of their progression from an initial screening, chemotherapy, surgery, and surveillance after resection. Radiomics serve as a diagnostic and prognostic tool, which can be applied at many different timepoints in oncologic care. Use of this technology in the diagnosis stage, treatment planning stage, and surveillance stage can complement traditional oncologic care and help personalize treatment options for patients with solid tumors [23].

Figure 2. This flowchart shows the generalized overview of the neoadjuvant and adjuvant studies from patient image acquisition (CT, MRI, PET), tumor segmentation, radiomic signature, machine learning analysis, and the predicted outcome. These studies capture prognosis, recurrence, therapeutic response, survival, and tumor volume. These various outcome categories describe different aspects of the effectiveness of either neoadjuvant or adjuvant chemotherapy [24,25].

Table 1. Summary of technical approaches for neoadjuvant studies.

Ref.	Pat.	Cancer	Modality	Feat. extracted	Feat. selected	Model
Wei et al. [24]	142	Ovarian	CT	620	76	KM, Cox
Lin et al. [28]	191	HOS	CT	540	8	Mult. LR
Drukker et al. [29]	158	Breast	MRI	49	18	LDA, 632+ Bootstrap
Braman et al. [30]	209	Breast	MRI	495	44	LDA
Li et al. [31]	30	Gastric	CT	19,985	–	SBFLDA + RF
Zhou et al. [32]	55	Breast	MRI	704	11	RF
Khalvati et al. [33]	98	PDAC	CT	410	3	KM, Wald, Cox
Wang et al. [34]	411	Rectal	CT	271	21	Mult. Modeling, NMF
Wang et al. [35]	146	GEJAC	CT	–	1	KM, Cox
Cohen et al. [36]	1383	Breast	MRI	349	14	RF, LR, XGBoost
Tian et al. [37]	277	Cervical	CT	1,294	6	LR
Liu et al. [38]	586	Breast	RMM	13,950	26	SVM

HOS: high-grade osteosarcoma; PDAC: pancreatic ductal adenocarcinoma; GEJAC: gastroesophageal junction adenocarcinoma; KM: Kaplain–Meier; LDA: linear discriminant analysis; LR: linear regression; Mult.: multivariate; NMF: non-negative matrix factorization; RF: random forest; SBFLDA: selection by filter based on linear discriminant analysis; SVM: support vector machine; XGBoost: extreme gradient boosting.

Table 2. Summary of technical approaches for adjuvant studies.

Ref.	Pat.	Cancer	Modality	Feat. extracted	Feat. selected	Model
Jiang et al. [39]	1778	Gastric	CT	584	13	LR, KM, Cox
Bourbonne et al. [40]	195	Prostate	MRI	–	1	LR, KM, Cox
Pavic et al. [41]	72	MPM	PET/CT	2,814	3	Cox
Spraker et al. [42]	165	STS	MRI	150	5	LASSO-Cox, KM, Cox
Bani-Sadr et al. [43]	76	GBM	MRI	39	11, 14	RF, KM, Cox
Jiang et al. [26]	1,591	Gastric	CT	269	19	LASSO-Cox, KM, Cox
Wormald et al. [44]	125	Cervical	MRI	–	10	LR
Xie et al. [45]	554	Lung	CT	107	8	LASSO-Cox, KM, Cox
Vaidya et al. [46]	556	Lung	CT	–	13	LASSO-Cox, KM, Cox

MPM: malignant pleural mesothelioma; STS: soft-tissue sarcoma; GBM: glioblastoma; KM: Kaplain–Meier; LASSO: least absolute shrinkage and selection operator; LR: linear regression; RF: random forest.

Table 3. Performance metrics from neoadjuvant studies in this review.

Ref.	Pred.	Sn	Sp	Acc	AUC	HR
Wei et al. [24]	Recurrence	–	–	–	0.820 0.700 0.889	–
Lin et al. [28]	pGR	–	–	–	0.823	–
Drukker et al. [29]	pCR	–	–	–	0.820	–
Braman et al. [30]	pCR	–	–	–	0.890	–
Li et al. [31]	non-GR	0.636	0.889	0.793	0.722	–
Zhou et al. [32]	pCR	0.762	0.845	0.810	0.888	–
Khalvati et al. [33]	Prognosis	–	–	–	–	1.56 (p = 0.005) 1.35 (p = 0.022)
Wang et al. [34]	OS	–	–	0.655 0.730	–	–
Wang et al. [35]	OS	–	–	–	–	1.61 (p = 0.040)
Cohen et al. [36]	pCR	0.954	0.222	–	0.708	–
Tian et al. [37]	tumor size	–	–	0.821	0.821	–
Liu et al. [38]	pCR	–	–	–	0.790	–

Table 4. Performance metrics from adjuvant studies in this review.

Ref.	Pred.	Sn	Sp	Acc	AUC	HR
Jiang et al. [39]	ISgc, RIS	–	–	–	0.786	0.339–0.605 (p < 0.003)
					0.745	0.436 (p = 0.002)
					0.766	0.591 (p < 0.001)
Bourbonne et al. [40]	Recurrence RFS	0.590	0.93	0.760	0.820	5.5 (p < 0.0001)
Pavic et al. [41] Spraker et al. [42]	OS PFS OS	– –	– –	– –	0.470 0.660 0.820	– 1.1–1.5 (p: 0.001–0.58)
Bani-Sadr et al. [43]	OS PFS	0.941	0.375	0.941	– 0.690	3.76 (p = 0.001) 3.58 (p = 0.004)
Jiang et al. [26]	DFS OS RS1 RS3 RS5	–	–	–	– − 0.822 0.754 0.821 0.726 0.804 0.821	3.137, 3.215 (p < 0.0001) 2.671, 2.830 (p < 0.0001)
Wormald et al. [44]	Recurrence	–	–	–	0.864 0.808	–
Xie et al. [45]	DFS	–	–	–	0.713	7.794 (p < 0.001)
Vaidya et al. [46]	DFS	–	–	–	–	1.56 (p = 0.016) 2.66 (p = 0.011) 2.67 (p = 0.0029)

Figure 3. This flowchart shows the image data processing and radiomic analysis used to parse and validate the radiomic features. LASSO: least absolute shrinkage and selection operator; LDA: linear discriminant analysis; RF: random forest; ROC: receiver operating characteristic.

Figure 4. This flowchart shows the generalized overview of the neoadjuvant and adjuvant studies from patient image acquisition (CT, MRI, PET), tumor segmentation, radiomic signature, machine learning analysis, and the predicted outcome in the traditional machine learning radiomics approach versus the deep learning approach.

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